14.5.10.9.3 Countering Adversarial Attacks, Defense, Robustness

Miller, D.J., Xiang, Z., Kesidis, G.,
Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks,
PIEEE(108), No. 3, March 2020, pp. 402-433.
IEEE DOI 2003
Training data, Neural networks, Reverse engineering, Machine learning, Robustness, Training data, Feature extraction, white box BibRef

Amini, S., Ghaemmaghami, S.,
Towards Improving Robustness of Deep Neural Networks to Adversarial Perturbations,
MultMed(22), No. 7, July 2020, pp. 1889-1903.
IEEE DOI 2007
Robustness, Perturbation methods, Training, Deep learning, Neural networks, Signal to noise ratio, interpretable BibRef

Li, X.R.[Xu-Rong], Ji, S.L.[Shou-Ling], Ji, J.T.[Jun-Tao], Ren, Z.Y.[Zhen-Yu], Wu, C.M.[Chun-Ming], Li, B.[Bo], Wang, T.[Ting],
Adversarial examples detection through the sensitivity in space mappings,
IET-CV(14), No. 5, August 2020, pp. 201-213.
DOI Link 2007
BibRef

Li, H., Zeng, Y., Li, G., Lin, L., Yu, Y.,
Online Alternate Generator Against Adversarial Attacks,
IP(29), 2020, pp. 9305-9315.
IEEE DOI 2010
Generators, Training, Perturbation methods, Knowledge engineering, Convolutional neural networks, Deep learning, image classification BibRef

Zhang, Y.G.[Yong-Gang], Tian, X.M.[Xin-Mei], Li, Y.[Ya], Wang, X.C.[Xin-Chao], Tao, D.C.[Da-Cheng],
Principal Component Adversarial Example,
IP(29), 2020, pp. 4804-4815.
IEEE DOI 2003
Manifolds, Neural networks, Perturbation methods, Distortion, Task analysis, Robustness, Principal component analysis, manifold learning BibRef

Ma, X.J.[Xing-Jun], Niu, Y.H.[Yu-Hao], Gu, L.[Lin], Wang, Y.S.[Yi-Sen], Zhao, Y.T.[Yi-Tian], Bailey, J.[James], Lu, F.[Feng],
Understanding adversarial attacks on deep learning based medical image analysis systems,
PR(110), 2021, pp. 107332.
Elsevier DOI 2011
Adversarial attack, Adversarial example detection, Medical image analysis, Deep learning BibRef

Zhou, M.[Mo], Niu, Z.X.[Zhen-Xing], Wang, L.[Le], Zhang, Q.L.[Qi-Lin], Hua, G.[Gang],
Adversarial Ranking Attack and Defense,
ECCV20(XIV:781-799).
Springer DOI 2011
BibRef

Agarwal, A.[Akshay], Vatsa, M.[Mayank], Singh, R.[Richa], Ratha, N.[Nalini],
Cognitive data augmentation for adversarial defense via pixel masking,
PRL(146), 2021, pp. 244-251.
Elsevier DOI 2105
Adversarial attacks, Deep learning, Data augmentation BibRef

Agarwal, A.[Akshay], Ratha, N.[Nalini], Vatsa, M.[Mayank], Singh, R.[Richa],
Exploring Robustness Connection between Artificial and Natural Adversarial Examples,
ArtOfRobust22(178-185)
IEEE DOI 2210
Deep learning, Neural networks, Semantics, Transformers, Robustness, Convolutional neural networks BibRef

Li, Z.R.[Zhuo-Rong], Feng, C.[Chao], Wu, M.H.[Ming-Hui], Yu, H.C.[Hong-Chuan], Zheng, J.W.[Jian-Wei], Zhu, F.[Fanwei],
Adversarial robustness via attention transfer,
PRL(146), 2021, pp. 172-178.
Elsevier DOI 2105
Adversarial defense, Robustness, Representation learning, Visual attention, Transfer learning BibRef

Zhang, S.D.[Shu-Dong], Gao, H.[Haichang], Rao, Q.X.[Qing-Xun],
Defense Against Adversarial Attacks by Reconstructing Images,
IP(30), 2021, pp. 6117-6129.
IEEE DOI 2107
Perturbation methods, Image reconstruction, Training, Iterative methods, Computational modeling, Predictive models, perceptual loss BibRef

Li, N.N.[Nan-Nan], Chen, Z.Z.[Zhen-Zhong],
Toward Visual Distortion in Black-Box Attacks,
IP(30), 2021, pp. 6156-6167.
IEEE DOI 2107
Distortion, Visualization, Measurement, Loss measurement, Optimization, Convergence, Training, Black-box attack, classification BibRef

Zhao, Z.Q.[Zhi-Qun], Wang, H.Y.[Heng-You], Sun, H.[Hao], Yuan, J.H.[Jian-He], Huang, Z.C.[Zhong-Chao], He, Z.H.[Zhi-Hai],
Removing Adversarial Noise via Low-Rank Completion of High-Sensitivity Points,
IP(30), 2021, pp. 6485-6497.
IEEE DOI 2107
Perturbation methods, Training, Neural networks, Image denoising, Optimization, TV, Sensitivity, Adversarial examples, TV norm BibRef

Hu, W.Z.[Wen-Zheng], Li, M.Y.[Ming-Yang], Wang, Z.[Zheng], Wang, J.Q.[Jian-Qiang], Zhang, C.S.[Chang-Shui],
DiFNet: Densely High-Frequency Convolutional Neural Networks,
SPLetters(28), 2021, pp. 1340-1344.
IEEE DOI 2107
Image edge detection, Convolution, Perturbation methods, Training, Neural networks, Robustness, Robust, deep convolution neural network BibRef

Mustafa, A.[Aamir], Khan, S.H.[Salman H.], Hayat, M.[Munawar], Goecke, R.[Roland], Shen, J.B.[Jian-Bing], Shao, L.[Ling],
Deeply Supervised Discriminative Learning for Adversarial Defense,
PAMI(43), No. 9, September 2021, pp. 3154-3166.
IEEE DOI 2108
Robustness, Perturbation methods, Training, Linear programming, Optimization, Marine vehicles, Prototypes, Adversarial defense, deep supervision BibRef

Karim, F.[Fazle], Majumdar, S.[Somshubra], Darabi, H.S.[Hou-Shang],
Adversarial Attacks on Time Series,
PAMI(43), No. 10, October 2021, pp. 3309-3320.
IEEE DOI 2109
Time series analysis, Computational modeling, Data models, Neural networks, Machine learning, Training, deep learning BibRef

Khodabakhsh, A.[Ali], Akhtar, Z.[Zahid],
Unknown presentation attack detection against rational attackers,
IET-Bio(10), No. 5, 2021, pp. 460-479.
DOI Link 2109
BibRef

Zhang, X.W.[Xing-Wei], Zheng, X.L.[Xiao-Long], Mao, W.J.[Wen-Ji],
Adversarial Perturbation Defense on Deep Neural Networks,
Surveys(54), No. 8, October 2021, pp. xx-yy.
DOI Link 2110
Survey, Adversarial Defense. security, deep neural networks, origin, Adversarial perturbation defense BibRef

Chen, X.[Xuan], Ma, Y.N.[Yue-Na], Lu, S.W.[Shi-Wei], Yao, Y.[Yu],
Boundary augment: A data augment method to defend poison attack,
IET-IPR(15), No. 13, 2021, pp. 3292-3303.
DOI Link 2110
BibRef

Xu, Y.H.[Yong-Hao], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification,
IP(30), 2021, pp. 8671-8685.
IEEE DOI 2110
Deep learning, Training, Hyperspectral imaging, Feature extraction, Task analysis, Perturbation methods, Predictive models, deep learning BibRef

Yu, H.[Hang], Liu, A.S.[Ai-Shan], Li, G.C.[Geng-Chao], Yang, J.C.[Ji-Chen], Zhang, C.Z.[Chong-Zhi],
Progressive Diversified Augmentation for General Robustness of DNNs: A Unified Approach,
IP(30), 2021, pp. 8955-8967.
IEEE DOI 2111
Robustness, Training, Handheld computers, Perturbation methods, Complexity theory, Streaming media, Standards BibRef

Prakash, C.D.[Charan D.], Karam, L.J.[Lina J.],
It GAN Do Better: GAN-Based Detection of Objects on Images With Varying Quality,
IP(30), 2021, pp. 9220-9230.
IEEE DOI 2112
Object detection, Training, Image quality, Computational modeling, Task analysis, Generators, Distortion, Object detection, GAN, RetinaNet BibRef

Niu, S.[Sijie], Qu, X.F.[Xiao-Feng], Chen, J.[Junting], Gao, X.[Xizhan], Wang, T.W.[Ting-Wei], Dong, J.W.[Ji-Wen],
MFNet-LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection,
IET-IPR(15), No. 14, 2021, pp. 3608-3622.
DOI Link 2112
BibRef

Dai, T.[Tao], Feng, Y.[Yan], Chen, B.[Bin], Lu, J.[Jian], Xia, S.T.[Shu-Tao],
Deep image prior based defense against adversarial examples,
PR(122), 2022, pp. 108249.
Elsevier DOI 2112
Deep neural network, Adversarial example, Image prior, Defense BibRef

Nguyen, H.H.[Huy H.], Kuribayashi, M.[Minoru], Yamagishi, J.[Junichi], Echizen, I.[Isao],
Effects of Image Processing Operations on Adversarial Noise and Their Use in Detecting and Correcting Adversarial Images,
IEICE(E105-D), No. 1, January 2022, pp. 65-77.
WWW Link. 2201
BibRef

Lo, S.Y.[Shao-Yuan], Patel, V.M.[Vishal M.],
Defending Against Multiple and Unforeseen Adversarial Videos,
IP(31), 2022, pp. 962-973.
IEEE DOI 2201
Videos, Training, Robustness, Perturbation methods, Resists, Image reconstruction, Image recognition, Adversarial video, multi-perturbation robustness BibRef

Gao, S.[Song], Yu, S.[Shui], Wu, L.W.[Li-Wen], Yao, S.W.[Shao-Wen], Zhou, X.W.[Xiao-Wei],
Detecting adversarial examples by additional evidence from noise domain,
IET-IPR(16), No. 2, 2022, pp. 378-392.
DOI Link 2201
BibRef

Wang, J.W.[Jin-Wei], Zhao, J.J.[Jun-Jie], Yin, Q.L.[Qi-Lin], Luo, X.Y.[Xiang-Yang], Zheng, Y.H.[Yu-Hui], Shi, Y.Q.[Yun-Qing], Jha, S.I.K.[Sun-Il Kr.],
SmsNet: A New Deep Convolutional Neural Network Model for Adversarial Example Detection,
MultMed(24), 2022, pp. 230-244.
IEEE DOI 2202
Feature extraction, Training, Manuals, Perturbation methods, Information science, Principal component analysis, SmsConnection BibRef

You, D.[Dan], Wang, S.[Shouguang], Seatzu, C.[Carla],
A Liveness-Enforcing Supervisor Tolerant to Sensor-Reading Modification Attacks,
SMCS(52), No. 4, April 2022, pp. 2398-2411.
IEEE DOI 2203
Robot sensing systems, Control systems, Actuators, Petri nets, Directed graphs, Supervisory control, Security, Attacks, Petri nets (PNs) BibRef

Mygdalis, V.[Vasileios], Pitas, I.[Ioannis],
Hyperspherical class prototypes for adversarial robustness,
PR(125), 2022, pp. 108527.
Elsevier DOI 2203
Adversarial defense, Adversarial robustness, Hypersphere prototype loss, HCP loss BibRef

Liang, Q.[Qi], Li, Q.[Qiang], Nie, W.Z.[Wei-Zhi],
LD-GAN: Learning perturbations for adversarial defense based on GAN structure,
SP:IC(103), 2022, pp. 116659.
Elsevier DOI 2203
Adversarial attacks, Adversarial defense, Adversarial robustness, Image classification BibRef

Shao, R.[Rui], Perera, P.[Pramuditha], Yuen, P.C.[Pong C.], Patel, V.M.[Vishal M.],
Open-Set Adversarial Defense with Clean-Adversarial Mutual Learning,
IJCV(130), No. 1, January 2022, pp. 1070-1087.
Springer DOI 2204
BibRef
Earlier:
Open-set Adversarial Defense,
ECCV20(XVII:682-698).
Springer DOI 2011
BibRef

Gao, L.L.[Lian-Li], Huang, Z.J.[Zi-Jie], Song, J.K.[Jing-Kuan], Yang, Y.[Yang], Shen, H.T.[Heng Tao],
Push & Pull: Transferable Adversarial Examples With Attentive Attack,
MultMed(24), 2022, pp. 2329-2338.
IEEE DOI 2205
Perturbation methods, Feature extraction, Computational modeling, Task analysis, Predictive models, Neural networks, targeted attack BibRef

Subramanyam, A.V.,
Sinkhorn Adversarial Attack and Defense,
IP(31), 2022, pp. 4039-4049.
IEEE DOI 2206
Iterative methods, Training, Perturbation methods, Loss measurement, Standards, Robustness, Linear programming, adversarial attack and defense BibRef

Khong, T.T.T.[Thi Thu Thao], Nakada, T.[Takashi], Nakashima, Y.[Yasuhiko],
A Hybrid Bayesian-Convolutional Neural Network for Adversarial Robustness,
IEICE(E105-D), No. 7, July 2022, pp. 1308-1319.
WWW Link. 2207
BibRef

Wang, K.[Ke], Li, F.J.[Feng-Jun], Chen, C.M.[Chien-Ming], Hassan, M.M.[Mohammad Mehedi], Long, J.Y.[Jin-Yi], Kumar, N.[Neeraj],
Interpreting Adversarial Examples and Robustness for Deep Learning-Based Auto-Driving Systems,
ITS(23), No. 7, July 2022, pp. 9755-9764.
IEEE DOI 2207
Training, Robustness, Deep learning, Perturbation methods, Interference, Computer science, Computational modeling, adversarial robustness BibRef

Wang, Y.Z.[Yuan-Zhe], Liu, Q.[Qipeng], Mihankhah, E.[Ehsan], Lv, C.[Chen], Wang, D.[Danwei],
Detection and Isolation of Sensor Attacks for Autonomous Vehicles: Framework, Algorithms, and Validation,
ITS(23), No. 7, July 2022, pp. 8247-8259.
IEEE DOI 2207
Robot sensing systems, Autonomous vehicles, Laser radar, Mathematical model, Detectors, Global Positioning System, cyber-attack BibRef

Wang, J.[Jia], Su, W.Q.[Wu-Qiang], Luo, C.W.[Cheng-Wen], Chen, J.[Jie], Song, H.B.[Hou-Bing], Li, J.Q.[Jian-Qiang],
CSG: Classifier-Aware Defense Strategy Based on Compressive Sensing and Generative Networks for Visual Recognition in Autonomous Vehicle Systems,
ITS(23), No. 7, July 2022, pp. 9543-9553.
IEEE DOI 2207
Training, Neural networks, Compressed sensing, Perturbation methods, Robustness, Real-time systems, generative neural networks BibRef

Wang, K.[Kun], Liu, M.Z.[Mao-Zhen],
YOLO-Anti: YOLO-based counterattack model for unseen congested object detection,
PR(131), 2022, pp. 108814.
Elsevier DOI 2208
Deep learning, Congested and occluded objects, Object detection BibRef

Xue, W.[Wei], Chen, Z.M.[Zhi-Ming], Tian, W.W.[Wei-Wei], Wu, Y.H.[Yun-Hua], Hua, B.[Bing],
A Cascade Defense Method for Multidomain Adversarial Attacks under Remote Sensing Detection,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Shi, X.S.[Xiao-Shuang], Peng, Y.F.[Yi-Fan], Chen, Q.Y.[Qing-Yu], Keenan, T.[Tiarnan], Thavikulwat, A.T.[Alisa T.], Lee, S.[Sungwon], Tang, Y.X.[Yu-Xing], Chew, E.Y.[Emily Y.], Summers, R.M.[Ronald M.], Lu, Z.Y.[Zhi-Yong],
Robust convolutional neural networks against adversarial attacks on medical images,
PR(132), 2022, pp. 108923.
Elsevier DOI 2209
CNNs, Adversarial examples, Sparsity denoising BibRef

Rakin, A.S.[Adnan Siraj], He, Z.[Zhezhi], Li, J.T.[Jing-Tao], Yao, F.[Fan], Chakrabarti, C.[Chaitali], Fan, D.L.[De-Liang],
T-BFA: Targeted Bit-Flip Adversarial Weight Attack,
PAMI(44), No. 11, November 2022, pp. 7928-7939.
IEEE DOI 2210
BibRef
Earlier: A2, A1, A3, A5, A6, Only:
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack,
CVPR20(14083-14091)
IEEE DOI 2008
Computational modeling, Random access memory, Computer security, Training, Quantization (signal), Data models, Memory management, bit-flip. Neural networks, Random access memory, Indexes, Optimization, Degradation, Immune system BibRef

Ji, S.[Sangwoo], Park, N.[Namgyu], Na, D.B.[Dong-Bin], Zhu, B.[Bin], Kim, J.[Jong],
Defending against attacks tailored to transfer learning via feature distancing,
CVIU(223), 2022, pp. 103533.
Elsevier DOI 2210
Robust transfer learning, Adversarial example, Triplet loss, Mimic attack, Target-agnostic attack BibRef

Melacci, S.[Stefano], Ciravegna, G.[Gabriele], Sotgiu, A.[Angelo], Demontis, A.[Ambra], Biggio, B.[Battista], Gori, M.[Marco], Roli, F.[Fabio],
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers,
PAMI(44), No. 12, December 2022, pp. 9944-9959.
IEEE DOI 2212
Training, Training data, Robustness, Task analysis, Adversarial machine learning, Ink, Semisupervised learning, multi-label classification BibRef

Pintor, M.[Maura], Angioni, D.[Daniele], Sotgiu, A.[Angelo], Demetrio, L.[Luca], Demontis, A.[Ambra], Biggio, B.[Battista], Roli, F.[Fabio],
ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches,
PR(134), 2023, pp. 109064.
Elsevier DOI 2212
Adversarial machine learning, Adversarial patches, Neural networks, Defense, Detection BibRef

Theagarajan, R.[Rajkumar], Bhanu, B.[Bir],
Privacy Preserving Defense For Black Box Classifiers Against On-Line Adversarial Attacks,
PAMI(44), No. 12, December 2022, pp. 9503-9520.
IEEE DOI 2212
Training, Perturbation methods, Bayes methods, Uncertainty, Deep learning, Privacy, Data models, Adversarial defense, privacy preserving defense BibRef

Yu, Y.J.[Young-Joon], Lee, H.J.[Hong Joo], Lee, H.[Hakmin], Ro, Y.M.[Yong Man],
Defending Person Detection Against Adversarial Patch Attack by Using Universal Defensive Frame,
IP(31), 2022, pp. 6976-6990.
IEEE DOI 2212
Detectors, Task analysis, Optimization, Robustness, Training, Security, Head, Adversarial patch, defensive pattern, person detection BibRef

Yu, X.[Xi], Smedemark-Margulies, N.[Niklas], Aeron, S.[Shuchin], Koike-Akino, T.[Toshiaki], Moulin, P.[Pierre], Brand, M.[Matthew], Parsons, K.[Kieran], Wang, Y.[Ye],
Improving adversarial robustness by learning shared information,
PR(134), 2023, pp. 109054.
Elsevier DOI 2212
Adversarial robustness, Information bottleneck, Multi-view learning, Shared information, BibRef

Machado, G.R.[Gabriel Resende], Silva, E.[Eugenio], Goldschmidt, R.R.[Ronaldo Ribeiro],
Adversarial Machine Learning in Image Classification: A Survey Toward the Defender's Perspective,
Surveys(55), No. 1, January 2023, pp. xx-yy.
DOI Link 2212
adversarial attacks, deep neural networks, adversarial images, Computer vision, defense methods, image classification BibRef

Rathore, H.[Hemant], Sasan, A.[Animesh], Sahay, S.K.[Sanjay K.], Sewak, M.[Mohit],
Defending malware detection models against evasion based adversarial attacks,
PRL(164), 2022, pp. 119-125.
Elsevier DOI 2212
Adversarial robustness, Deep neural network, Evasion attack, Malware analysis and detection, Machine learning BibRef

Goldblum, M.[Micah], Tsipras, D.[Dimitris], Xie, C.[Chulin], Chen, X.Y.[Xin-Yun], Schwarzschild, A.[Avi], Song, D.[Dawn], Madry, A.[Aleksander], Li, B.[Bo], Goldstein, T.[Tom],
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses,
PAMI(45), No. 2, February 2023, pp. 1563-1580.
IEEE DOI 2301
Data models, Training, Training data, Security, Toxicology, Unsolicited e-mail, Servers, Data poisoning, backdoor attacks, dataset security BibRef

Lee, S.[Sungyoon], Kim, H.[Hoki], Lee, J.W.[Jae-Wook],
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization,
PAMI(45), No. 2, February 2023, pp. 2645-2651.
IEEE DOI 2301
Neural networks, Robustness, Stochastic processes, Perturbation methods, Training, Transform coding, Statistics, directional analysis BibRef

Lin, D.[Da], Wang, Y.G.[Yuan-Gen], Tang, W.X.[Wei-Xuan], Kang, X.G.[Xian-Gui],
Boosting Query Efficiency of Meta Attack With Dynamic Fine-Tuning,
SPLetters(29), 2022, pp. 2557-2561.
IEEE DOI 2301
Distortion, Optimization, Estimation, Training, Tuning, Closed box, Rate distortion theory, Adversarial attack, query efficiency BibRef

Seo, S.[Seungwan], Lee, Y.[Yunseung], Kang, P.[Pilsung],
Cost-free adversarial defense: Distance-based optimization for model robustness without adversarial training,
CVIU(227), 2023, pp. 103599.
Elsevier DOI 2301
Adversarial defense, White-box attack, Adversarial robustness, Distance-based defense BibRef

Cheng, Z.[Zhen], Zhu, F.[Fei], Zhang, X.Y.[Xu-Yao], Liu, C.L.[Cheng-Lin],
Adversarial training with distribution normalization and margin balance,
PR(136), 2023, pp. 109182.
Elsevier DOI 2301
Adversarial robustness, Adversarial training, Distribution normalization, Margin balance BibRef

Zhou, S.[Shuai], Liu, C.[Chi], Ye, D.[Dayong], Zhu, T.Q.[Tian-Qing], Zhou, W.[Wanlei], Yu, P.S.[Philip S.],
Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity,
Surveys(55), No. 8, December 2022, pp. xx-yy.
DOI Link 2301
cybersecurity, adversarial attacks and defenses, advanced persistent threats, Deep learning BibRef

Picot, M.[Marine], Messina, F.[Francisco], Boudiaf, M.[Malik], Labeau, F.[Fabrice], Ben Ayed, I.[Ismail], Piantanida, P.[Pablo],
Adversarial Robustness Via Fisher-Rao Regularization,
PAMI(45), No. 3, March 2023, pp. 2698-2710.
IEEE DOI 2302
Robustness, Manifolds, Training, Perturbation methods, Standards, Neural networks, Adversarial machine learning, safety AI BibRef

Stutz, D.[David], Chandramoorthy, N.[Nandhini], Hein, M.[Matthias], Schiele, B.[Bernt],
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators,
PAMI(45), No. 3, March 2023, pp. 3632-3647.
IEEE DOI 2302
Robustness, Quantization (signal), Random access memory, Training, Voltage, Bit error rate, Low voltage, DNN Accelerators, DNN quantization BibRef

Stutz, D.[David], Hein, M.[Matthias], Schiele, B.[Bernt],
Disentangling Adversarial Robustness and Generalization,
CVPR19(6969-6980).
IEEE DOI 2002
BibRef

Guo, Y.[Yong], Stutz, D.[David], Schiele, B.[Bernt],
Improving Robustness by Enhancing Weak Subnets,
ECCV22(XXIV:320-338).
Springer DOI 2211
BibRef

Guo, J.[Jun], Bao, W.[Wei], Wang, J.[Jiakai], Ma, Y.Q.[Yu-Qing], Gao, X.[Xinghai], Xiao, G.[Gang], Liu, A.[Aishan], Dong, J.[Jian], Liu, X.L.[Xiang-Long], Wu, W.J.[Wen-Jun],
A comprehensive evaluation framework for deep model robustness,
PR(137), 2023, pp. 109308.
Elsevier DOI 2302
Adversarial examples, Evaluation metrics, Model robustness BibRef

Niu, Z.H.[Zhong-Han], Yang, Y.B.[Yu-Bin],
Defense Against Adversarial Attacks with Efficient Frequency-Adaptive Compression and Reconstruction,
PR(138), 2023, pp. 109382.
Elsevier DOI 2303
Deep neural networks, Adversarial defense, Adversarial robustness, Closed-set attack, Open-set attack BibRef

Zhang, J.J.[Jia-Jin], Chao, H.Q.[Han-Qing], Yan, P.K.[Ping-Kun],
Toward Adversarial Robustness in Unlabeled Target Domains,
IP(32), 2023, pp. 1272-1284.
IEEE DOI 2303
Training, Robustness, Adaptation models, Data models, Deep learning, Task analysis, Labeling, Adversarial robustness, domain adaptation, pseudo labeling BibRef

Liu, D.Z.[Dai-Zong], Hu, W.[Wei],
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification,
PAMI(45), No. 4, April 2023, pp. 4727-4746.
IEEE DOI 2303
Point cloud compression, Solid modeling, Perturbation methods, Data models, Distortion, Atmospheric modeling, defense on adversarial attacks BibRef

Brau, F.[Fabio], Rossolini, G.[Giulio], Biondi, A.[Alessandro], Buttazzo, G.[Giorgio],
On the Minimal Adversarial Perturbation for Deep Neural Networks With Provable Estimation Error,
PAMI(45), No. 4, April 2023, pp. 5038-5052.
IEEE DOI 2303
Perturbation methods, Robustness, Estimation, Neural networks, Deep learning, Error analysis, Computational modeling, verification methods BibRef

Xiang, W.Z.[Wen-Zhao], Su, H.[Hang], Liu, C.[Chang], Guo, Y.D.[Yan-Dong], Zheng, S.[Shibao],
Improving the robustness of adversarial attacks using an affine-invariant gradient estimator,
CVIU(229), 2023, pp. 103647.
Elsevier DOI 2303
Adversarial attack, Deep neural networks, Affine invariance, Transferability BibRef

Zhang, Y.[Yu], Gong, Z.Q.[Zhi-Qiang], Zhang, Y.C.[Yi-Chuang], Bin, K.C.[Kang-Cheng], Li, Y.Q.[Yong-Qian], Qi, J.H.[Jia-Hao], Wen, H.[Hao], Zhong, P.[Ping],
Boosting transferability of physical attack against detectors by redistributing separable attention,
PR(138), 2023, pp. 109435.
Elsevier DOI 2303
Physical attack, Transferability, Multi-layer attention, Object detection, Black-box models BibRef

Quan, C.[Chen], Sriranga, N.[Nandan], Yang, H.D.[Hao-Dong], Han, Y.H.S.[Yung-Hsiang S.], Geng, B.C.[Bao-Cheng], Varshney, P.K.[Pramod K.],
Efficient Ordered-Transmission Based Distributed Detection Under Data Falsification Attacks,
SPLetters(30), 2023, pp. 145-149.
IEEE DOI 2303
Energy efficiency, Wireless sensor networks, Upper bound, Optimization, Distributed databases, Simulation, distributed detection BibRef

Park, J.[Jeongeun], Shin, S.[Seungyoun], Hwang, S.[Sangheum], Choi, S.[Sungjoon],
Elucidating robust learning with uncertainty-aware corruption pattern estimation,
PR(138), 2023, pp. 109387.
Elsevier DOI 2303
Robust learning, Training with noisy labels, Uncertainty estimation, Corruption pattern estimation BibRef

Wang, Z.[Zhen], Wang, B.H.[Bu-Hong], Zhang, C.L.[Chuan-Lei], Liu, Y.[Yaohui],
Defense against Adversarial Patch Attacks for Aerial Image Semantic Segmentation by Robust Feature Extraction,
RS(15), No. 6, 2023, pp. 1690.
DOI Link 2304
BibRef

Wang, Z.[Zhen], Wang, B.H.[Bu-Hong], Zhang, C.L.[Chuan-Lei], Liu, Y.[Yaohui], Guo, J.X.[Jian-Xin],
Robust Feature-Guided Generative Adversarial Network for Aerial Image Semantic Segmentation against Backdoor Attacks,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Wang, Z.[Zhen], Wang, B.H.[Bu-Hong], Zhang, C.L.[Chuan-Lei], Liu, Y.[Yaohui], Guo, J.X.[Jian-Xin],
Defending against Poisoning Attacks in Aerial Image Semantic Segmentation with Robust Invariant Feature Enhancement,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Naseer, M.[Muzammal], Khan, S.[Salman], Hayat, M.[Munawar], Khan, F.S.[Fahad Shahbaz], Porikli, F.M.[Fatih M.],
Stylized Adversarial Defense,
PAMI(45), No. 5, May 2023, pp. 6403-6414.
IEEE DOI 2304
Training, Perturbation methods, Robustness, Multitasking, Predictive models, Computational modeling, Visualization, multi-task objective BibRef

Xu, Q.Q.[Qian-Qian], Yang, Z.Y.[Zhi-Yong], Zhao, Y.R.[Yun-Rui], Cao, X.C.[Xiao-Chun], Huang, Q.M.[Qing-Ming],
Rethinking Label Flipping Attack: From Sample Masking to Sample Thresholding,
PAMI(45), No. 6, June 2023, pp. 7668-7685.
IEEE DOI 2305
Data models, Training data, Training, Deep learning, Predictive models, Testing, Optimization, Label flipping attack, machine learning BibRef

Zago, J.G.[João G.], Antonelo, E.A.[Eric A.], Baldissera, F.L.[Fabio L.], Saad, R.T.[Rodrigo T.],
Benford's law: What does it say on adversarial images?,
JVCIR(93), 2023, pp. 103818.
Elsevier DOI 2305
Benford's law, Adversarial attacks, Convolutional neural networks, Adversarial detection BibRef

Li, W.[Wen], Wang, H.[Hengyou], Huo, L.[Lianzhi], He, Q.[Qiang], Zhang, C.[Changlun],
Robust attention ranking architecture with frequency-domain transform to defend against adversarial samples,
CVIU(233), 2023, pp. 103717.
Elsevier DOI 2307
Adversarial samples, Attention mechanism, Discrete cosine transform, Key points ranking BibRef

Zhang, Y.X.[Yu-Xuan], Meng, H.[Hua], Cao, X.M.[Xue-Mei], Zhou, Z.C.[Zheng-Chun], Yang, M.[Mei], Adhikary, A.R.[Avik Ranjan],
Interpreting vulnerabilities of multi-instance learning to adversarial perturbations,
PR(142), 2023, pp. 109725.
Elsevier DOI 2307
Customized perturbation, Multi-instance learning, Universal perturbation, Vulnerability BibRef

Dong, J.[Junhao], Yang, L.X.[Ling-Xiao], Wang, Y.[Yuan], Xie, X.H.[Xiao-Hua], Lai, J.H.[Jian-Huang],
Toward Intrinsic Adversarial Robustness Through Probabilistic Training,
IP(32), 2023, pp. 3862-3872.
IEEE DOI 2307
Training, Uncertainty, Probabilistic logic, Robustness, Standards, Computational modeling, Feature extraction, Deep neural networks, uncertainty BibRef

Shi, M.C.[Meng-Chen], Xie, F.[Fei], Yang, J.Q.[Ji-Quan], Zhao, J.[Jing], Liu, X.X.[Xi-Xiang], Wang, F.[Fan],
Cutout with patch-loss augmentation for improving generative adversarial networks against instability,
CVIU(234), 2023, pp. 103761.
Elsevier DOI 2307
Generative Adversarial Networks, Dataset augmentation, Convolution neural network BibRef

Lee, H.[Hakmin], Ro, Y.M.[Yong Man],
Adversarial anchor-guided feature refinement for adversarial defense,
IVC(136), 2023, pp. 104722.
Elsevier DOI 2308
Adversarial example, Adversarial robustness, Adversarial anchor, Covariate shift, Feature refinement BibRef

Gao, W.[Wei], Zhang, X.[Xu], Guo, S.[Shangwei], Zhang, T.W.[Tian-Wei], Xiang, T.[Tao], Qiu, H.[Han], Wen, Y.G.[Yong-Gang], Liu, Y.[Yang],
Automatic Transformation Search Against Deep Leakage From Gradients,
PAMI(45), No. 9, September 2023, pp. 10650-10668.
IEEE DOI Collaborative learning, deal with attacks that reveal shared data. 2309
BibRef

Wei, X.X.[Xing-Xing], Wang, S.[Songping], Yan, H.Q.[Huan-Qian],
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos,
PAMI(45), No. 9, September 2023, pp. 10898-10912.
IEEE DOI 2309
BibRef

Saini, N.[Nandini], Chattopadhyay, C.[Chiranjoy], Das, D.[Debasis],
SOLARNet: A single stage regression based framework for efficient and robust object recognition in aerial images,
PRL(172), 2023, pp. 37-43.
Elsevier DOI 2309
Adversarial attacks, Deep learning, Aerial image, Object detection, DOTA, DIOR BibRef

Chandna, K.[Kshitij],
Improving Adversarial Robustness by Penalizing Natural Accuracy,
AdvRob22(517-533).
Springer DOI 2304
BibRef

Zhao, Z.Y.[Zheng-Yu], Dang, N.[Nga], Larson, M.[Martha],
The Importance of Image Interpretation: Patterns of Semantic Misclassification in Real-world Adversarial Images,
MMMod23(II: 718-725).
Springer DOI 2304
BibRef

Wang, J.H.[Jing-Hao], Cui, C.L.[Chen-Ling], Wen, X.J.[Xue-Jun], Shi, J.[Jie],
Transpatch: A Transformer-based Generator for Accelerating Transferable Patch Generation in Adversarial Attacks Against Object Detection Models,
AdvRob22(317-331).
Springer DOI 2304
BibRef

Venkatesh, R.[Rahul], Wong, E.[Eric], Kolter, Z.[Zico],
Adversarial robustness in discontinuous spaces via alternating sampling and descent,
WACV23(4651-4660)
IEEE DOI 2302
Training, Solid modeling, Perturbation methods, Pipelines, Predictive models, Search problems, visual reasoning BibRef

Nayak, G.K.[Gaurav Kumar], Rawal, R.[Ruchit], Chakraborty, A.[Anirban],
DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers,
WACV23(4611-4620)
IEEE DOI 2302
Deep learning, Smoothing methods, Costs, Neural networks, Training data, Robustness, Algorithms: Adversarial learning BibRef

Kakizaki, K.[Kazuya], Fukuchi, K.[Kazuto], Sakuma, J.[Jun],
Certified Defense for Content Based Image Retrieval,
WACV23(4550-4559)
IEEE DOI 2302
Training, Deep learning, Image retrieval, Neural networks, Linear programming, Feature extraction, visual reasoning BibRef

Zheng, Z.H.[Zhi-Hao], Ying, X.W.[Xiao-Wen], Yao, Z.[Zhen], Chuah, M.C.[Mooi Choo],
Robustness of Trajectory Prediction Models Under Map-Based Attacks,
WACV23(4530-4539)
IEEE DOI 2302
Visualization, Image coding, Sensitivity analysis, Computational modeling, Predictive models, Control systems, adversarial attack and defense methods BibRef

Mathur, A.N.[Aradhya Neeraj], Madan, A.[Anish], Sharma, O.[Ojaswa],
SLI-pSp: Injecting Multi-Scale Spatial Layout in pSp,
WACV23(4084-4093)
IEEE DOI 2302
Visualization, Image synthesis, Layout, Generators, Task analysis, Algorithms: Computational photography, adversarial attack and defense methods BibRef

Xu, K.[Ke], Xiao, Y.[Yao], Zheng, Z.H.[Zhao-Heng], Cai, K.[Kaijie], Nevatia, R.[Ram],
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch,
WACV23(4621-4630)
IEEE DOI 2302
Training, Degradation, Shape, Pipelines, Neural networks, Object detection, Robustness, Algorithms: Adversarial learning, visual reasoning BibRef

Dargaud, L.[Laurine], Ibsen, M.[Mathias], Tapia, J.[Juan], Busch, C.[Christoph],
A Principal Component Analysis-Based Approach for Single Morphing Attack Detection,
Explain-Bio23(683-692)
IEEE DOI 2302
Training, Learning systems, Visualization, Image color analysis, Feature extraction, Human in the loop, Detection algorithms BibRef

Drenkow, N.[Nathan], Lennon, M.[Max], Wang, I.J.[I-Jeng], Burlina, P.[Philippe],
Do Adaptive Active Attacks Pose Greater Risk Than Static Attacks?,
WACV23(1380-1389)
IEEE DOI 2302
Measurement, Sensitivity analysis, Aggregates, Kinematics, Observers, Trajectory, Algorithms: Adversarial learning, visual reasoning BibRef

Chen, Y.K.[Yong-Kang], Zhang, M.[Ming], Li, J.[Jin], Kuang, X.H.[Xiao-Hui],
Adversarial Attacks and Defenses in Image Classification: A Practical Perspective,
ICIVC22(424-430)
IEEE DOI 2301
Training, Deep learning, Benchmark testing, Market research, Security, Image classification, deep learning, security, defenses BibRef

Beetham, J.[James], Kardan, N.[Navid], Mian, A.[Ajmal], Shah, M.[Mubarak],
Detecting Compromised Architecture/Weights of a Deep Model,
ICPR22(2843-2849)
IEEE DOI 2212
Smoothing methods, Perturbation methods, Closed box, Detectors, Predictive models, Data models BibRef

Hwang, D.[Duhun], Lee, E.[Eunjung], Rhee, W.[Wonjong],
AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense,
ICPR22(2401-2407)
IEEE DOI 2212
Training, Codes, Purification, Boosting, Robustness BibRef

Tasaki, H.[Hajime], Kaneko, Y.[Yuji], Chao, J.H.[Jin-Hui],
Curse of co-Dimensionality: Explaining Adversarial Examples by Embedding Geometry of Data Manifold,
ICPR22(2364-2370)
IEEE DOI 2212
Manifolds, Geometry, Training, Deep learning, Neural networks, Training data BibRef

Aithal, M.B.[Manjushree B.], Li, X.H.[Xiao-Hua],
Boundary Defense Against Black-box Adversarial Attacks,
ICPR22(2349-2356)
IEEE DOI 2212
Degradation, Limiting, Gaussian noise, Neural networks, Closed box, Reliability theory BibRef

Choi, J.H.[Jun-Ho], Zhang, H.[Huan], Kim, J.H.[Jun-Hyuk], Hsieh, C.J.[Cho-Jui], Lee, J.S.[Jong-Seok],
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks,
ICPR22(1287-1293)
IEEE DOI 2212
Degradation, Training, Analytical models, Perturbation methods, Noise reduction, Robustness BibRef

Modas, A.[Apostolos], Rade, R.[Rahul], Ortiz-Jiménez, G.[Guillermo], Moosavi-Dezfooli, S.M.[Seyed-Mohsen], Frossard, P.[Pascal],
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions,
ECCV22(XXV:623-640).
Springer DOI 2211
BibRef

Khalsi, R.[Rania], Smati, I.[Imen], Sallami, M.M.[Mallek Mziou], Ghorbel, F.[Faouzi],
A Novel System for Deep Contour Classifiers Certification Under Filtering Attacks,
ICIP22(3561-3565)
IEEE DOI 2211
Deep learning, Upper bound, Image recognition, Filtering, Perturbation methods, Robustness, Kernel, Contours classification, Uncertainty in AI BibRef

Zhang, Y.X.[Yu-Xuan], Dong, B.[Bo], Heide, F.[Felix],
All You Need Is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines,
ECCV22(XIX:323-343).
Springer DOI 2211
BibRef

Byun, J.[Junyoung], Go, H.[Hyojun], Cho, S.[Seungju], Kim, C.[Changick],
Exploiting Doubly Adversarial Examples for Improving Adversarial Robustness,
ICIP22(1331-1335)
IEEE DOI 2211
Training, Deep learning, Neural networks, Training data, Robustness, Adversarial training, Robustness BibRef

Lu, B.[Bingyi], Liu, J.Y.[Ji-Yuan], Xiong, H.L.[Hui-Lin],
Transformation-Based Adversarial Defense Via Sparse Representation,
ICIP22(1726-1730)
IEEE DOI 2211
Bridges, Training, Deep learning, Dictionaries, Perturbation methods, Neural networks, adversarial examples, adversarial defense, image classification BibRef

Subramanyam, A.V., Raj, A.[Abhigyan],
Barycentric Defense,
ICIP22(2276-2280)
IEEE DOI 2211
Training, Codes, Extraterrestrial measurements, Robustness, Barycenter, Dual Wasserstein, Adversarial defense BibRef

Wang, Z.[Zi], Li, C.C.[Cheng-Cheng], Li, H.[Husheng],
Adversarial Training of Anti-Distilled Neural Network with Semantic Regulation of Class Confidence,
ICIP22(3576-3580)
IEEE DOI 2211
Training, Knowledge engineering, Image coding, Semantics, Neural networks, Intellectual property, Regulation, Semantic similarity BibRef

Yin, X.[Xuwang], Li, S.Y.[Shi-Ying], Rohde, G.K.[Gustavo K.],
Learning Energy-Based Models with Adversarial Training,
ECCV22(V:209-226).
Springer DOI 2211
BibRef

Yang, S.[Shuo], Xu, C.[Chang],
One Size Does NOT Fit All: Data-Adaptive Adversarial Training,
ECCV22(V:70-85).
Springer DOI 2211
BibRef

Zheng, R.K.[Run-Kai], Tang, R.J.[Rong-Jun], Li, J.Z.[Jian-Ze], Liu, L.[Li],
Data-Free Backdoor Removal Based on Channel Lipschitzness,
ECCV22(V:175-191).
Springer DOI 2211
BibRef

Dolatabadi, H.M.[Hadi M.], Erfani, S.[Sarah], Leckie, C.[Christopher],
Collider: A Robust Training Framework for Backdoor Data,
ACCV22(VI:681-698).
Springer DOI 2307
BibRef

Dolatabadi, H.M.[Hadi M.], Erfani, S.[Sarah], Leckie, C.[Christopher],
l8-Robustness and Beyond: Unleashing Efficient Adversarial Training,
ECCV22(XI:467-483).
Springer DOI 2211
BibRef

Do, K.[Kien], Harikumar, H.[Haripriya], Le, H.[Hung], Nguyen, D.[Dung], Tran, T.[Truyen], Rana, S.[Santu], Nguyen, D.[Dang], Susilo, W.[Willy], Venkatesh, S.[Svetha],
Towards Effective and Robust Neural Trojan Defenses via Input Filtering,
ECCV22(V:283-300).
Springer DOI 2211
BibRef

Yang, X.[Xiao], Dong, Y.P.[Yin-Peng], Pang, T.Y.[Tian-Yu], Su, H.[Hang], Zhu, J.[Jun],
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks,
ECCV22(IV:725-742).
Springer DOI 2211
BibRef

Sun, J.C.[Jia-Chen], Mehra, A.[Akshay], Kailkhura, B.[Bhavya], Chen, P.Y.[Pin-Yu], Hendrycks, D.[Dan], Hamm, J.[Jihun], Mao, Z.M.[Z. Morley],
A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness,
ECCV22(IV:654-671).
Springer DOI 2211
BibRef

Li, G.L.[Guan-Lin], Xu, G.W.[Guo-Wen], Qiu, H.[Han], He, R.[Ruan], Li, J.[Jiwei], Zhang, T.W.[Tian-Wei],
Improving Adversarial Robustness of 3D Point Cloud Classification Models,
ECCV22(IV:672-689).
Springer DOI 2211
BibRef

Kowalski, C.[Charles], Famili, A.[Azadeh], Lao, Y.J.[Ying-Jie],
Towards Model Quantization on the Resilience Against Membership Inference Attacks,
ICIP22(3646-3650)
IEEE DOI 2211
Resistance, Performance evaluation, Privacy, Quantization (signal), Computational modeling, Neural networks, Training data, Neural Network BibRef

Ji, H.X.[Hu-Xiao], Li, J.[Jie], Wu, C.[Chentao],
CRAB: Certified Patch Robustness Against Poisoning-Based Backdoor Attacks,
ICIP22(2486-2490)
IEEE DOI 2211
Training, Deep learning, Smoothing methods, Neural networks, Games, Robustness, Computer Vision, Backdoor Attack, Certified Robustness, (De)randomized Smoothing BibRef

Ji, Y.[Yimu], Ding, J.Y.[Jian-Yu], Chen, Z.[Zhiyu], Wu, F.[Fei], Zhang, C.[Chi], Sun, Y.M.[Yi-Ming], Sun, J.[Jing], Liu, S.D.[Shang-Dong],
Simulator Attack+ for Black-Box Adversarial Attack,
ICIP22(636-640)
IEEE DOI 2211
Deep learning, Codes, Perturbation methods, Neural networks, Usability, Meta-learning, Adversarial Attack, Black-box Attack BibRef

Liang, S.Y.[Si-Yuan], Li, L.K.[Long-Kang], Fan, Y.B.[Yan-Bo], Jia, X.J.[Xiao-Jun], Li, J.Z.[Jing-Zhi], Wu, B.Y.[Bao-Yuan], Cao, X.C.[Xiao-Chun],
A Large-Scale Multiple-Objective Method for Black-box Attack Against Object Detection,
ECCV22(IV:619-636).
Springer DOI 2211
BibRef

Nayak, G.K.[Gaurav Kumar], Rawal, R.[Ruchit], Lal, R.[Rohit], Patil, H.[Himanshu], Chakraborty, A.[Anirban],
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems,
HCIS22(4331-4340)
IEEE DOI 2210
Measurement, Training, Knowledge engineering, Predictive models, Reliability engineering BibRef

Chen, Y.W.[Yu-Wei],
Rethinking Adversarial Examples in Wargames,
ArtOfRobust22(100-106)
IEEE DOI 2210
Neural networks, Decision making, Games, Prediction algorithms, Software, Pattern recognition, Security BibRef

Thakur, N.[Nupur], Li, B.X.[Bao-Xin],
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos,
ArtOfRobust22(130-137)
IEEE DOI 2210
Training, Deep learning, Perturbation methods, Surveillance, Gaussian noise, Neural networks BibRef

Lovisotto, G.[Giulio], Finnie, N.[Nicole], Munoz, M.[Mauricio], Murnmadi, C.K.[Chaithanya Kumar], Metzen, J.H.[Jan Hendrik],
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness,
CVPR22(15213-15222)
IEEE DOI 2210
Image recognition, Object detection, Transformer cores, Transformers, Robustness, Cognition, Machine learning BibRef

Haque, M.[Mirazul], Budnik, C.J.[Christof J.], Yang, W.[Wei],
CorrGAN: Input Transformation Technique Against Natural Corruptions,
ArtOfRobust22(193-196)
IEEE DOI 2210
Deep learning, Perturbation methods, Neural networks, Generative adversarial networks BibRef

Ren, S.C.[Su-Cheng], Gao, Z.Q.[Zheng-Qi], Hua, T.Y.[Tian-Yu], Xue, Z.[Zihui], Tian, Y.L.[Yong-Long], He, S.F.[Sheng-Feng], Zhao, H.[Hang],
Co-advise: Cross Inductive Bias Distillation,
CVPR22(16752-16761)
IEEE DOI 2210
Training, Representation learning, Convolutional codes, Convolution, Transformers, Adversarial attack and defense BibRef

Asnani, V.[Vishal], Yin, X.[Xi], Hassner, T.[Tal], Liu, S.[Sijia], Liu, X.M.[Xiao-Ming],
Proactive Image Manipulation Detection,
CVPR22(15365-15374)
IEEE DOI 2210
Training, Codes, Cameras, Pattern recognition, Detection algorithms, Adversarial attack and defense, Low-level vision BibRef

Pang, T.Y.[Tian-Yu], Zhang, H.[Huishuai], He, D.[Di], Dong, Y.P.[Yin-Peng], Su, H.[Hang], Chen, W.[Wei], Zhu, J.[Jun], Liu, T. .Y.[Tie- Yan],
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart,
CVPR22(15202-15212)
IEEE DOI 2210
Measurement, Training, Couplings, Machine learning, Predictive models, Robustness, Adversarial attack and defense, Machine learning BibRef

Li, K.[Kaidong], Zhang, Z.[Ziming], Zhong, C.[Cuncong], Wang, G.H.[Guang-Hui],
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients,
CVPR22(15273-15283)
IEEE DOI 2210
Point cloud compression, Deep learning, Image coding, Neural networks, Lattices, Pattern recognition, Deep learning architectures and techniques BibRef

Ren, Q.B.[Qi-Bing], Bao, Q.Q.[Qing-Quan], Wang, R.Z.[Run-Zhong], Yan, J.C.[Jun-Chi],
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond,
CVPR22(15242-15251)
IEEE DOI 2210
Training, Visualization, Image recognition, Computational modeling, Robustness, Data models, Adversarial attack and defense, Representation learning BibRef

Vellaichamy, S.[Sivapriya], Hull, M.[Matthew], Wang, Z.J.[Zijie J.], Das, N.[Nilaksh], Peng, S.[ShengYun], Park, H.[Haekyu], Chau, D.H.P.[Duen Horng Polo],
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors,
CVPR22(21452-21459)
IEEE DOI 2210
Visualization, Head, Detectors, Object detection, Feature extraction, Magnetic heads, Behavioral sciences BibRef

Lee, B.K.[Byung-Kwan], Kim, J.[Junho], Ro, Y.M.[Yong Man],
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network,
CVPR22(15105-15115)
IEEE DOI 2210
Training, Degradation, Computational modeling, Semantics, Neural networks, Memory management, Robustness, Adversarial attack and defense BibRef

Liu, Y.[Ye], Cheng, Y.[Yaya], Gao, L.[Lianli], Liu, X.L.[Xiang-Long], Zhang, Q.L.[Qi-Long], Song, J.[Jingkuan],
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack,
CVPR22(15084-15093)
IEEE DOI 2210
Adaptation models, Codes, Computational modeling, Robustness, Pattern recognition, Iterative methods, Adversarial attack and defense BibRef

Liu, Y.Q.[Ying-Qi], Shen, G.Y.[Guang-Yu], Tao, G.H.[Guan-Hong], Wang, Z.T.[Zhen-Ting], Ma, S.Q.[Shi-Qing], Zhang, X.Y.[Xiang-Yu],
Complex Backdoor Detection by Symmetric Feature Differencing,
CVPR22(14983-14993)
IEEE DOI 2210
Rendering (computer graphics), Feature extraction, Reflection, Pattern recognition, Adversarial attack and defense BibRef

Cai, Z.[Zikui], Rane, S.[Shantanu], Brito, A.E.[Alejandro E.], Song, C.Y.[Cheng-Yu], Krishnamurthy, S.V.[Srikanth V.], Roy-Chowdhury, A.K.[Amit K.], Asif, M.S.[M. Salman],
Zero-Query Transfer Attacks on Context-Aware Object Detectors,
CVPR22(15004-15014)
IEEE DOI 2210
Deep learning, Technological innovation, Image analysis, Perturbation methods, Neural networks, Detectors, Scene analysis and understanding BibRef

Liu, J.[Jiang], Levine, A.[Alexander], Lau, C.P.[Chun Pong], Chellappa, R.[Rama], Feizi, S.[Soheil],
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection,
CVPR22(14953-14962)
IEEE DOI 2210
Training, Location awareness, Image segmentation, Shape, Detectors, Object detection, Adversarial attack and defense BibRef

Özdenizci, O.[Ozan], Legenstein, R.[Robert],
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching,
CVPR22(13378-13387)
IEEE DOI 2210
Deep learning, Codes, Quantization (signal), Impedance matching, Computational modeling, Benchmark testing, Deep learning architectures and techniques BibRef

Jia, X.J.[Xiao-Jun], Zhang, Y.[Yong], Wu, B.Y.[Bao-Yuan], Ma, K.[Ke], Wang, J.[Jue], Cao, X.C.[Xiao-Chun],
LAS-AT: Adversarial Training with Learnable Attack Strategy,
CVPR22(13388-13398)
IEEE DOI 2210
Training, Codes, Databases, Benchmark testing, Robustness, Pattern recognition, Adversarial attack and defense BibRef

Li, T.[Tao], Wu, Y.[Yingwen], Chen, S.[Sizhe], Fang, K.[Kun], Huang, X.L.[Xiao-Lin],
Subspace Adversarial Training,
CVPR22(13399-13408)
IEEE DOI 2210
Training, Codes, Solids, Robustness, Pattern recognition, Standards, Adversarial attack and defense, Machine learning, Optimization methods BibRef

Guan, J.[Jiyang], Tu, Z.[Zhuozhuo], He, R.[Ran], Tao, D.C.[Da-Cheng],
Few-shot Backdoor Defense Using Shapley Estimation,
CVPR22(13348-13357)
IEEE DOI 2210
Deep learning, Training, Neurons, Neural networks, Estimation, Data models, Robustness, Adversarial attack and defense BibRef

Tao, G.H.[Guan-Hong], Shen, G.Y.[Guang-Yu], Liu, Y.Q.[Ying-Qi], An, S.W.[Sheng-Wei], Xu, Q.L.[Qiu-Ling], Ma, S.Q.[Shi-Qing], Li, P.[Pan], Zhang, X.Y.[Xiang-Yu],
Better Trigger Inversion Optimization in Backdoor Scanning,
CVPR22(13358-13368)
IEEE DOI 2210
Training, Computational modeling, Optimization methods, Robustness, Pattern recognition, Adversarial attack and defense, Optimization methods BibRef

Dong, J.H.[Jun-Hao], Wang, Y.[Yuan], Lai, J.H.[Jian-Huang], Xie, X.H.[Xiao-Hua],
Improving Adversarially Robust Few-shot Image Classification with Generalizable Representations,
CVPR22(9015-9024)
IEEE DOI 2210
Training, Deep learning, Image recognition, Benchmark testing, Pattern recognition, Task analysis, Adversarial attack and defense BibRef

Yamada, Y.[Yutaro], Otani, M.[Mayu],
Does Robustness on ImageNet Transfer to Downstream Tasks?,
CVPR22(9205-9214)
IEEE DOI 2210
Image segmentation, Transfer learning, Semantics, Neural networks, Object detection, Transformers, Robustness, Adversarial attack and defense BibRef

Li, J.T.[Jing-Tao], Rakin, A.S.[Adnan Siraj], Chen, X.[Xing], He, Z.[Zhezhi], Fan, D.L.[De-Liang], Chakrabarti, C.[Chaitali],
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning,
CVPR22(10184-10192)
IEEE DOI 2210
Training, Resistance, Federated learning, Computational modeling, Feature extraction, Pattern recognition, Servers, privacy and ethics in vision BibRef

Mao, X.F.[Xiao-Feng], Qi, G.[Gege], Chen, Y.[Yuefeng], Li, X.D.[Xiao-Dan], Duan, R.[Ranjie], Ye, S.[Shaokai], He, Y.[Yuan], Xue, H.[Hui],
Towards Robust Vision Transformer,
CVPR22(12032-12041)
IEEE DOI 2210
Systematics, Costs, Machine vision, Training data, Benchmark testing, Transformers, Robustness, Adversarial attack and defense BibRef

Chen, T.L.[Tian-Long], Zhang, Z.Y.[Zhen-Yu], Zhang, Y.H.[Yi-Hua], Chang, S.Y.[Shi-Yu], Liu, S.[Sijia], Wang, Z.Y.[Zhang-Yang],
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free,
CVPR22(588-599)
IEEE DOI 2210
Training, Deep learning, Neural networks, Training data, Network architecture, Pattern recognition, Adversarial attack and defense BibRef

Sun, M.J.[Ming-Jie], Li, Z.C.[Zi-Chao], Xiao, C.W.[Chao-Wei], Qiu, H.[Haonan], Kailkhura, B.[Bhavya], Liu, M.Y.[Ming-Yan], Li, B.[Bo],
Can Shape Structure Features Improve Model Robustness under Diverse Adversarial Settings?,
ICCV21(7506-7515)
IEEE DOI 2203
Visualization, Systematics, Sensitivity, Shape, Image edge detection, Perturbation methods, Pipelines, Adversarial learning, Recognition and classification BibRef

Poursaeed, O.[Omid], Jiang, T.X.[Tian-Xing], Yang, H.[Harry], Belongie, S.[Serge], Lim, S.N.[Ser-Nam],
Robustness and Generalization via Generative Adversarial Training,
ICCV21(15691-15700)
IEEE DOI 2203
Training, Deep learning, Image segmentation, Computational modeling, Neural networks, Object detection, Neural generative models BibRef

Yu, C.[Cheng], Chen, J.S.[Jian-Sheng], Xue, Y.[Youze], Liu, Y.Y.[Yu-Yang], Wan, W.T.[Wei-Tao], Bao, J.Y.[Jia-Yu], Ma, H.M.[Hui-Min],
Defending against Universal Adversarial Patches by Clipping Feature Norms,
ICCV21(16414-16422)
IEEE DOI 2203
Training, Visualization, Computational modeling, Robustness, Convolutional neural networks, Recognition and classification BibRef

Zhu, L.[Liuwan], Ning, R.[Rui], Xin, C.S.[Chun-Sheng], Wang, C.G.[Chong-Gang], Wu, H.Y.[Hong-Yi],
CLEAR: Clean-up Sample-Targeted Backdoor in Neural Networks,
ICCV21(16433-16442)
IEEE DOI 2203
Deep learning, Computational modeling, Neural networks, Benchmark testing, Feature extraction, Safety, BibRef

Zeng, Y.[Yi], Park, W.[Won], Mao, Z.M.[Z. Morley], Jia, R.[Ruoxi],
Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective,
ICCV21(16453-16461)
IEEE DOI 2203
Deep learning, Frequency-domain analysis, Detectors, Data models, Security, Adversarial learning, Image and video manipulation detection and integrity methods. BibRef

Dong, Y.P.[Yin-Peng], Yang, X.[Xiao], Deng, Z.J.[Zhi-Jie], Pang, T.Y.[Tian-Yu], Xiao, Z.[Zihao], Su, H.[Hang], Zhu, J.[Jun],
Black-box Detection of Backdoor Attacks with Limited Information and Data,
ICCV21(16462-16471)
IEEE DOI 2203
Training, Deep learning, Neural networks, Training data, Predictive models, Prediction algorithms, Adversarial learning, BibRef

Wang, X.P.[Xue-Ping], Li, S.[Shasha], Liu, M.[Min], Wang, Y.[Yaonan], Roy-Chowdhury, A.K.[Amit K.],
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency,
ICCV21(15077-15087)
IEEE DOI 2203
Deep learning, Perturbation methods, Neural networks, Detectors, Feature extraction, Context modeling, Image and video retrieval BibRef

Huang, J.X.[Jia-Xing], Guan, D.[Dayan], Xiao, A.[Aoran], Lu, S.J.[Shi-Jian],
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking,
ICCV21(8968-8979)
IEEE DOI 2203
Training, Representation learning, Perturbation methods, Semantics, Supervised learning, FAA, grouping and shape BibRef

Zhou, D.W.[Da-Wei], Wang, N.N.[Nan-Nan], Peng, C.L.[Chun-Lei], Gao, X.B.[Xin-Bo], Wang, X.Y.[Xiao-Yu], Yu, J.[Jun], Liu, T.L.[Tong-Liang],
Removing Adversarial Noise in Class Activation Feature Space,
ICCV21(7858-7867)
IEEE DOI 2203
Training, Deep learning, Adaptation models, Perturbation methods, Computational modeling, Noise reduction, Adversarial learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Benz, P.[Philipp], Zhang, C.N.[Chao-Ning], Kweon, I.S.[In So],
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective,
ICCV21(7798-7807)
IEEE DOI 2203
Radio frequency, Training, Integrated circuits, Deep learning, Costs, Neural networks, Adversarial learning, Explainable AI BibRef

Yin, M.J.[Ming-Jun], Li, S.[Shasha], Cai, Z.[Zikui], Song, C.Y.[Cheng-Yu], Asif, M.S.[M. Salman], Roy-Chowdhury, A.K.[Amit K.], Krishnamurthy, S.V.[Srikanth V.],
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes,
ICCV21(7838-7847)
IEEE DOI 2203
Deep learning, Machine vision, Computational modeling, Neural networks, Detectors, Context modeling, Adversarial learning, Scene analysis and understanding BibRef

Abusnaina, A.[Ahmed], Wu, Y.H.[Yu-Hang], Arora, S.[Sunpreet], Wang, Y.Z.[Yi-Zhen], Wang, F.[Fei], Yang, H.[Hao], Mohaisen, D.[David],
Adversarial Example Detection Using Latent Neighborhood Graph,
ICCV21(7667-7676)
IEEE DOI 2203
Training, Manifolds, Deep learning, Network topology, Perturbation methods, Neural networks, Adversarial learning, Recognition and classification BibRef

Mao, C.Z.[Cheng-Zhi], Chiquier, M.[Mia], Wang, H.[Hao], Yang, J.F.[Jun-Feng], Vondrick, C.[Carl],
Adversarial Attacks are Reversible with Natural Supervision,
ICCV21(641-651)
IEEE DOI 2203
Training, Benchmark testing, Robustness, Inference algorithms, Image restoration, Recognition and classification, Adversarial learning BibRef

Zhao, X.J.[Xue-Jun], Zhang, W.C.[Wen-Can], Xiao, X.K.[Xiao-Kui], Lim, B.[Brian],
Exploiting Explanations for Model Inversion Attacks,
ICCV21(662-672)
IEEE DOI 2203
Privacy, Semantics, Data visualization, Medical services, Predictive models, Data models, Artificial intelligence, Recognition and classification BibRef

Wang, Q.[Qian], Kurz, D.[Daniel],
Reconstructing Training Data from Diverse ML Models by Ensemble Inversion,
WACV22(3870-3878)
IEEE DOI 2202
Training, Analytical models, Filtering, Training data, Machine learning, Predictive models, Security/Surveillance BibRef

Tursynbek, N.[Nurislam], Petiushko, A.[Aleksandr], Oseledets, I.[Ivan],
Geometry-Inspired Top-k Adversarial Perturbations,
WACV22(4059-4068)
IEEE DOI 2202
Perturbation methods, Prediction algorithms, Multitasking, Classification algorithms, Task analysis, Adversarial Attack and Defense Methods BibRef

Nayak, G.K.[Gaurav Kumar], Rawal, R.[Ruchit], Chakraborty, A.[Anirban],
DAD: Data-free Adversarial Defense at Test Time,
WACV22(3788-3797)
IEEE DOI 2202
Training, Adaptation models, Biological system modeling, Frequency-domain analysis, Training data, Adversarial Attack and Defense Methods BibRef

Byun, J.[Junyoung], Go, H.[Hyojun], Kim, C.[Changick],
On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks,
WACV22(3819-3828)
IEEE DOI 2202
Deep learning, Codes, Additives, Computational modeling, Neural networks, Estimation, Adversarial Attack and Defense Methods Deep Learning BibRef

Scheliga, D.[Daniel], Mäder, P.[Patrick], Seeland, M.[Marco],
PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage,
WACV22(3605-3614)
IEEE DOI 2202
Training, Privacy, Data privacy, Perturbation methods, Computational modeling, Training data, Stochastic processes, Deep Learning Gradient Inversion Attacks BibRef

Wang, S.J.[Shao-Jie], Wu, T.[Tong], Chakrabarti, A.[Ayan], Vorobeychik, Y.[Yevgeniy],
Adversarial Robustness of Deep Sensor Fusion Models,
WACV22(1371-1380)
IEEE DOI 2202
Training, Systematics, Laser radar, Perturbation methods, Neural networks, Object detection, Sensor fusion, Adversarial Attack and Defense Methods BibRef

Nesti, F.[Federico], Rossolini, G.[Giulio], Nair, S.[Saasha], Biondi, A.[Alessandro], Buttazzo, G.[Giorgio],
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks,
WACV22(2826-2835)
IEEE DOI 2202
Computational modeling, Perturbation methods, Semantics, Pipelines, Grouping and Shape BibRef

Drenkow, N.[Nathan], Fendley, N.[Neil], Burlina, P.[Philippe],
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis,
WACV22(2815-2825)
IEEE DOI 2202
Training, Performance evaluation, Perturbation methods, Training data, Detectors, Feature extraction, Security/Surveillance BibRef

Cheng, H.[Hao], Xu, K.D.[Kai-Di], Li, Z.G.[Zhen-Gang], Zhao, P.[Pu], Wang, C.[Chenan], Lin, X.[Xue], Kailkhura, B.[Bhavya], Goldhahn, R.[Ryan],
More or Less (MoL): Defending against Multiple Perturbation Attacks on Deep Neural Networks through Model Ensemble and Compression,
Hazards22(645-655)
IEEE DOI 2202
Training, Deep learning, Perturbation methods, Computational modeling, Conferences, Neural networks BibRef

Lang, I.[Itai], Kotlicki, U.[Uriel], Avidan, S.[Shai],
Geometric Adversarial Attacks and Defenses on 3D Point Clouds,
3DV21(1196-1205)
IEEE DOI 2201
Point cloud compression, Geometry, Deep learning, Solid modeling, Shape, Semantics, 3D Point Clouds, Geometry Processing, Defense Methods BibRef

Hasnat, A.[Abul], Shvai, N.[Nadiya], Nakib, A.[Amir],
CNN Classifier's Robustness Enhancement when Preserving Privacy,
ICIP21(3887-3891)
IEEE DOI 2201
Privacy, Data privacy, Image processing, Supervised learning, Prediction algorithms, Robustness, Privacy, Vehicle Classification, CNN BibRef

Liu, L.Q.[Lan-Qing], Duan, Z.Y.[Zhen-Yu], Xu, G.Z.[Guo-Zheng], Xu, Y.[Yi],
Self-Supervised Disentangled Embedding for Robust Image Classification,
ICIP21(1494-1498)
IEEE DOI 2201
Deep learning, Image segmentation, Correlation, Target recognition, Tools, Robustness, Security, Disentanglement, Adversarial Examples, Robustness BibRef

Chu, T.S.[Tian-Shu], Yang, Z.P.[Zuo-Peng], Yang, J.[Jie], Huang, X.L.[Xiao-Lin],
Improving the Robustness of Convolutional Neural Networks Via Sketch Attention,
ICIP21(869-873)
IEEE DOI 2201
Training, Perturbation methods, Image processing, Pipelines, Robustness, Convolutional neural networks, CNNs, sketch attention BibRef

Deng, K.[Kang], Peng, A.[Anjie], Dong, W.L.[Wan-Li], Zeng, H.[Hui],
Detecting C &W Adversarial Images Based on Noise Addition-Then-Denoising,
ICIP21(3607-3611)
IEEE DOI 2201
Deep learning, Visualization, Perturbation methods, Gaussian noise, Image processing, Noise reduction, Deep neural network, Detection BibRef

Maho, T.[Thibault], Bonnet, B.[Benoît], Furony, T.[Teddy], Le Merrer, E.[Erwan],
RoBIC: A Benchmark Suite for Assessing Classifiers Robustness,
ICIP21(3612-3616)
IEEE DOI 2201
Image processing, Benchmark testing, Distortion, Robustness, Distortion measurement, Benchmark, adversarial examples, half-distortion measure BibRef

Wang, Y.P.[Yao-Peng], Xie, L.[Lehui], Liu, X.M.[Xi-Meng], Yin, J.L.[Jia-Li], Zheng, T.J.[Ting-Jie],
Model-Agnostic Adversarial Example Detection Through Logit Distribution Learning,
ICIP21(3617-3621)
IEEE DOI 2201
Deep learning, Resistance, Semantics, Feature extraction, Task analysis, deep learning, adversarial detector, adversarial defenses BibRef

Raj, A.[Ankita], Pal, A.[Ambar], Arora, C.[Chetan],
Identifying Physically Realizable Triggers for Backdoored Face Recognition Networks,
ICIP21(3023-3027)
IEEE DOI 2201
Deep learning, Image recognition, Face recognition, Force, Adversarial attack, trojan attack, back-door attack, face recognition BibRef

Co, K.T.[Kenneth T.], Muñoz-González, L.[Luis], Kanthan, L.[Leslie], Glocker, B.[Ben], Lupu, E.C.[Emil C.],
Universal Adversarial Robustness of Texture and Shape-Biased Models,
ICIP21(799-803)
IEEE DOI 2201
Training, Deep learning, Analytical models, Perturbation methods, Image processing, Neural networks, deep neural networks BibRef

Xu, W.P.[Wei-Peng], Huang, H.C.[Hong-Cheng], Pan, S.Y.[Shao-You],
Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness In Object Detection,
ICIP21(2184-2188)
IEEE DOI 2201
Training, Object detection, Detectors, Feature extraction, Robustness, deep learning, object detection, adversarial training BibRef

Agarwal, A.[Akshay], Vatsa, M.[Mayank], Singh, R.[Richa], Ratha, N.[Nalini],
Intelligent and Adaptive Mixup Technique for Adversarial Robustness,
ICIP21(824-828)
IEEE DOI 2201
Training, Deep learning, Image recognition, Image analysis, Perturbation methods, Robustness, Natural language processing, Object Recognition BibRef

Yu, C.[Cheng], Xue, Y.Z.[You-Ze], Chen, J.S.[Jian-Sheng], Wang, Y.[Yu], Ma, H.M.[Hui-Min],
Enhancing Adversarial Robustness for Image Classification By Regularizing Class Level Feature Distribution,
ICIP21(494-498)
IEEE DOI 2201
Training, Deep learning, Adaptation models, Image processing, Neural networks, Robustness, Adversarial Training, Robustness BibRef

Chai, W.H.[Wei-Heng], Lu, Y.T.[Yan-Tao], Velipasalar, S.[Senem],
Weighted Average Precision: Adversarial Example Detection for Visual Perception of Autonomous Vehicles,
ICIP21(804-808)
IEEE DOI 2201
Measurement, Perturbation methods, Image processing, Pipelines, Neural networks, Optimization methods, Object detection, Neural Networks BibRef

Kung, B.H.[Bo-Han], Chen, P.C.[Pin-Chun], Liu, Y.C.[Yu-Cheng], Chen, J.C.[Jun-Cheng],
Squeeze and Reconstruct: Improved Practical Adversarial Defense Using Paired Image Compression and Reconstruction,
ICIP21(849-853)
IEEE DOI 2201
Training, Deep learning, Image coding, Perturbation methods, Transform coding, Robustness, Adversarial Attack, JPEG Compression, Artifact Correction BibRef

Li, C.Y.[Chau Yi], Sánchez-Matilla, R.[Ricardo], Shamsabadi, A.S.[Ali Shahin], Mazzon, R.[Riccardo], Cavallaro, A.[Andrea],
On the Reversibility of Adversarial Attacks,
ICIP21(3073-3077)
IEEE DOI 2201
Deep learning, Perturbation methods, Image processing, Benchmark testing, Adversarial perturbations, Reversibility BibRef

Bakiskan, C.[Can], Cekic, M.[Metehan], Sezer, A.D.[Ahmet Dundar], Madhow, U.[Upamanyu],
A Neuro-Inspired Autoencoding Defense Against Adversarial Attacks,
ICIP21(3922-3926)
IEEE DOI 2201
Training, Deep learning, Image coding, Perturbation methods, Neural networks, Decoding, Adversarial, Machine learning, Robust, Defense BibRef

Pérez, J.C.[Juan C.], Alfarra, M.[Motasem], Jeanneret, G.[Guillaume], Rueda, L.[Laura], Thabet, A.[Ali], Ghanem, B.[Bernard], Arbeláez, P.[Pablo],
Enhancing Adversarial Robustness via Test-Time Transformation Ensembling,
AROW21(81-91)
IEEE DOI 2112
Deep learning, Perturbation methods, Transforms, Robustness, Data models BibRef

Zhang, C.[Cheng], Gao, P.[Pan],
Countering Adversarial Examples: Combining Input Transformation and Noisy Training,
AROW21(102-111)
IEEE DOI 2112
Training, Image coding, Quantization (signal), Perturbation methods, Computational modeling, Transform coding, Artificial neural networks BibRef

De, K.[Kanjar], Pedersen, M.[Marius],
Impact of Colour on Robustness of Deep Neural Networks,
AROW21(21-30)
IEEE DOI 2112
Deep learning, Image color analysis, Perturbation methods, Tools, Distortion, Robustness BibRef

Truong, J.B.[Jean-Baptiste], Maini, P.[Pratyush], Walls, R.J.[Robert J.], Papernot, N.[Nicolas],
Data-Free Model Extraction,
CVPR21(4769-4778)
IEEE DOI 2111
Adaptation models, Computational modeling, Intellectual property, Predictive models, Data models, Complexity theory BibRef

Mehra, A.[Akshay], Kailkhura, B.[Bhavya], Chen, P.Y.[Pin-Yu], Hamm, J.[Jihun],
How Robust are Randomized Smoothing based Defenses to Data Poisoning?,
CVPR21(13239-13248)
IEEE DOI 2111
Training, Deep learning, Smoothing methods, Toxicology, Perturbation methods, Distortion, Robustness BibRef

Deng, Z.J.[Zhi-Jie], Yang, X.[Xiao], Xu, S.Z.[Shi-Zhen], Su, H.[Hang], Zhu, J.[Jun],
LiBRe: A Practical Bayesian Approach to Adversarial Detection,
CVPR21(972-982)
IEEE DOI 2111
Training, Deep learning, Costs, Uncertainty, Neural networks, Bayes methods, Pattern recognition BibRef

Yang, K.[Karren], Lin, W.Y.[Wan-Yi], Barman, M.[Manash], Condessa, F.[Filipe], Kolter, Z.[Zico],
Defending Multimodal Fusion Models against Single-Source Adversaries,
CVPR21(3339-3348)
IEEE DOI 2111
Training, Sentiment analysis, Perturbation methods, Neural networks, Object detection, Robustness BibRef

Wu, T.[Tong], Liu, Z.[Ziwei], Huang, Q.Q.[Qing-Qiu], Wang, Y.[Yu], Lin, D.[Dahua],
Adversarial Robustness under Long-Tailed Distribution,
CVPR21(8655-8664)
IEEE DOI 2111
Training, Systematics, Codes, Robustness, Pattern recognition BibRef

Ong, D.S.[Ding Sheng], Chan, C.S.[Chee Seng], Ng, K.W.[Kam Woh], Fan, L.X.[Li-Xin], Yang, Q.[Qiang],
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attacks,
CVPR21(3629-3638)
IEEE DOI 2111
Deep learning, Knowledge engineering, Image synthesis, Superresolution, Intellectual property, Watermarking BibRef

Addepalli, S.[Sravanti], Jain, S.[Samyak], Sriramanan, G.[Gaurang], Babu, R.V.[R. Venkatesh],
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing,
SAIAD21(93-102)
IEEE DOI 2109
Training, Deep learning, Smoothing methods, Boosting, Feature extraction BibRef

Pestana, C.[Camilo], Liu, W.[Wei], Glance, D.[David], Mian, A.[Ajmal],
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty,
WACV21(556-565)
IEEE DOI 2106
Measurement, Deep learning, Machine learning algorithms, Image recognition BibRef

Ali, A.[Arslan], Migliorati, A.[Andrea], Bianchi, T.[Tiziano], Magli, E.[Enrico],
Beyond Cross-Entropy: Learning Highly Separable Feature Distributions for Robust and Accurate Classification,
ICPR21(9711-9718)
IEEE DOI 2105
Robustness to adversarial attacks. Training, Deep learning, Perturbation methods, Gaussian distribution, Linear programming, Robustness BibRef

Kyatham, V.[Vinay], Mishra, D.[Deepak], Prathosh, A.P.,
Variational Inference with Latent Space Quantization for Adversarial Resilience,
ICPR21(9593-9600)
IEEE DOI 2105
Manifolds, Degradation, Quantization (signal), Perturbation methods, Neural networks, Data models, Real-time systems BibRef

Li, H.[Honglin], Fan, Y.F.[Yi-Fei], Ganz, F.[Frieder], Yezzi, A.J.[Anthony J.], Barnaghi, P.[Payam],
Verifying the Causes of Adversarial Examples,
ICPR21(6750-6757)
IEEE DOI 2105
Geometry, Perturbation methods, Neural networks, Linearity, Estimation, Aerospace electronics, Probabilistic logic BibRef

Hou, Y.F.[Yu-Fan], Zou, L.X.[Li-Xin], Liu, W.D.[Wei-Dong],
Task-based Focal Loss for Adversarially Robust Meta-Learning,
ICPR21(2824-2829)
IEEE DOI 2105
Training, Perturbation methods, Resists, Machine learning, Benchmark testing, Robustness BibRef

Huang, Y.T.[Yen-Ting], Liao, W.H.[Wen-Hung], Huang, C.W.[Chen-Wei],
Defense Mechanism Against Adversarial Attacks Using Density-based Representation of Images,
ICPR21(3499-3504)
IEEE DOI 2105
Deep learning, Perturbation methods, Transforms, Hybrid power systems, Pattern recognition, Intelligent systems BibRef

Chhabra, S.[Saheb], Agarwal, A.[Akshay], Singh, R.[Richa], Vatsa, M.[Mayank],
Attack Agnostic Adversarial Defense via Visual Imperceptible Bound,
ICPR21(5302-5309)
IEEE DOI 2105
Visualization, Sensitivity, Databases, Computational modeling, Perturbation methods, Predictive models, Prediction algorithms BibRef

Šircelj, J.[Jaka], Skocaj, D.[Danijel],
Accuracy-Perturbation Curves for Evaluation of Adversarial Attack and Defence Methods,
ICPR21(6290-6297)
IEEE DOI 2105
Training, Visualization, Perturbation methods, Machine learning, Robustness, Generators, Pattern recognition BibRef

Watson, M.[Matthew], Moubayed, N.A.[Noura Al],
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning,
ICPR21(8180-8187)
IEEE DOI 2105
Training, Deep learning, Perturbation methods, MIMICs, Medical services, Predictive models, Feature extraction, Medical Data BibRef

Alamri, F.[Faisal], Kalkan, S.[Sinan], Pugeault, N.[Nicolas],
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection,
ICPR21(9577-9584)
IEEE DOI 2105
Visualization, Perturbation methods, Detectors, Object detection, Transforms, Field-flow fractionation, Feature extraction BibRef

Bouniot, Q.[Quentin], Audigier, R.[Romaric], Loesch, A.[Angelique],
Optimal Transport as a Defense Against Adversarial Attacks,
ICPR21(5044-5051)
IEEE DOI 2105
Training, Deep learning, Measurement, Adaptation models, Perturbation methods, Image representation, Market research BibRef

Schwartz, D.[Daniel], Alparslan, Y.[Yigit], Kim, E.[Edward],
Regularization and Sparsity for Adversarial Robustness and Stable Attribution,
ISVC20(I:3-14).
Springer DOI 2103
BibRef

Carrara, F.[Fabio], Caldelli, R.[Roberto], Falchi, F.[Fabrizio], Amato, G.[Giuseppe],
Defending Neural ODE Image Classifiers from Adversarial Attacks with Tolerance Randomization,
MMForWild20(425-438).
Springer DOI 2103
BibRef

Gittings, T., Schneider, S., Collomosse, J.,
Vax-a-net: Training-time Defence Against Adversarial Patch Attacks,
ACCV20(IV:235-251).
Springer DOI 2103
BibRef

Yi, C., Li, H., Wan, R., Kot, A.C.,
Improving Robustness of DNNs against Common Corruptions via Gaussian Adversarial Training,
VCIP20(17-20)
IEEE DOI 2102
Robustness, Perturbation methods, Training, Neural networks, Standards, Gaussian noise, Tensors, Deep Learning, Data Augmentation BibRef

Rusak, E.[Evgenia], Schott, L.[Lukas], Zimmermann, R.S.[Roland S.], Bitterwolf, J.[Julian], Bringmann, O.[Oliver], Bethge, M.[Matthias], Brendel, W.[Wieland],
A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions,
ECCV20(III:53-69).
Springer DOI 2012
BibRef

Li, Y.W.[Ying-Wei], Bai, S.[Song], Xie, C.H.[Ci-Hang], Liao, Z.Y.[Zhen-Yu], Shen, X.H.[Xiao-Hui], Yuille, A.L.[Alan L.],
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses,
ECCV20(XI:795-813).
Springer DOI 2011
BibRef

Bui, A.[Anh], Le, T.[Trung], Zhao, H.[He], Montague, P.[Paul], deVel, O.[Olivier], Abraham, T.[Tamas], Phung, D.[Dinh],
Improving Adversarial Robustness by Enforcing Local and Global Compactness,
ECCV20(XXVII:209-223).
Springer DOI 2011
BibRef

Xu, J., Li, Y., Jiang, Y., Xia, S.T.,
Adversarial Defense Via Local Flatness Regularization,
ICIP20(2196-2200)
IEEE DOI 2011
Training, Standards, Perturbation methods, Robustness, Visualization, Linearity, Taylor series, adversarial defense, gradient-based regularization BibRef

Maung, M., Pyone, A., Kiya, H.,
Encryption Inspired Adversarial Defense For Visual Classification,
ICIP20(1681-1685)
IEEE DOI 2011
Training, Transforms, Encryption, Perturbation methods, Machine learning, Adversarial defense, perceptual image encryption BibRef

Shah, S.A.A., Bougre, M., Akhtar, N., Bennamoun, M., Zhang, L.,
Efficient Detection of Pixel-Level Adversarial Attacks,
ICIP20(718-722)
IEEE DOI 2011
Robots, Training, Perturbation methods, Machine learning, Robustness, Task analysis, Testing, Adversarial attack, perturbation detection, deep learning BibRef

Jia, S.[Shuai], Ma, C.[Chao], Song, Y.B.[Yi-Bing], Yang, X.K.[Xiao-Kang],
Robust Tracking Against Adversarial Attacks,
ECCV20(XIX:69-84).
Springer DOI 2011
BibRef

Wang, R.[Ren], Zhang, G.Y.[Gao-Yuan], Liu, S.J.[Si-Jia], Chen, P.Y.[Pin-Yu], Xiong, J.J.[Jin-Jun], Wang, M.[Meng],
Practical Detection of Trojan Neural Networks: Data-limited and Data-free Cases,
ECCV20(XXIII:222-238).
Springer DOI 2011
(or poisoning backdoor attack) Manipulate the learned network. BibRef

Mao, C.Z.[Cheng-Zhi], Cha, A.[Augustine], Gupta, A.[Amogh], Wang, H.[Hao], Yang, J.F.[Jun-Feng], Vondrick, C.[Carl],
Generative Interventions for Causal Learning,
CVPR21(3946-3955)
IEEE DOI 2111
Training, Visualization, Correlation, Computational modeling, Control systems, Pattern recognition BibRef

Mao, C.Z.[Cheng-Zhi], Gupta, A.[Amogh], Nitin, V.[Vikram], Ray, B.[Baishakhi], Song, S.[Shuran], Yang, J.F.[Jun-Feng], Vondrick, C.[Carl],
Multitask Learning Strengthens Adversarial Robustness,
ECCV20(II:158-174).
Springer DOI 2011
BibRef

Li, S.S.[Sha-Sha], Zhu, S.T.[Shi-Tong], Paul, S.[Sudipta], Roy-Chowdhury, A.K.[Amit K.], Song, C.Y.[Cheng-Yu], Krishnamurthy, S.[Srikanth], Swami, A.[Ananthram], Chan, K.S.[Kevin S.],
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency,
ECCV20(XXIII:396-413).
Springer DOI 2011
BibRef

Li, Y.[Yueru], Cheng, S.Y.[Shu-Yu], Su, H.[Hang], Zhu, J.[Jun],
Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds,
ECCV20(XXVIII:753-769).
Springer DOI 2011
BibRef

Rounds, J.[Jeremiah], Kingsland, A.[Addie], Henry, M.J.[Michael J.], Duskin, K.R.[Kayla R.],
Probing for Artifacts: Detecting Imagenet Model Evasions,
AML-CV20(3432-3441)
IEEE DOI 2008
Perturbation methods, Probes, Computational modeling, Robustness, Image color analysis, Machine learning, Indexes BibRef

Kariyappa, S., Qureshi, M.K.,
Defending Against Model Stealing Attacks With Adaptive Misinformation,
CVPR20(767-775)
IEEE DOI 2008
Data models, Adaptation models, Cloning, Predictive models, Computational modeling, Security, Perturbation methods BibRef

Mohapatra, J., Weng, T., Chen, P., Liu, S., Daniel, L.,
Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations,
CVPR20(241-249)
IEEE DOI 2008
Semantics, Perturbation methods, Robustness, Image color analysis, Brightness, Neural networks, Tools BibRef

Liu, X., Xiao, T., Si, S., Cao, Q., Kumar, S., Hsieh, C.,
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework,
CVPR20(279-287)
IEEE DOI 2008
Neural networks, Robustness, Stochastic processes, Training, Random variables, Gaussian noise, Mathematical model BibRef

Wu, M., Kwiatkowska, M.,
Robustness Guarantees for Deep Neural Networks on Videos,
CVPR20(308-317)
IEEE DOI 2008
Robustness, Videos, Optical imaging, Adaptive optics, Optical sensors, Measurement, Neural networks BibRef

Chan, A., Tay, Y., Ong, Y.,
What It Thinks Is Important Is Important: Robustness Transfers Through Input Gradients,
CVPR20(329-338)
IEEE DOI 2008
Robustness, Task analysis, Training, Computational modeling, Perturbation methods, Impedance matching, Predictive models BibRef

Zhang, L., Yu, M., Chen, T., Shi, Z., Bao, C., Ma, K.,
Auxiliary Training: Towards Accurate and Robust Models,
CVPR20(369-378)
IEEE DOI 2008
Training, Robustness, Perturbation methods, Neural networks, Data models, Task analysis, Feature extraction BibRef

Saha, A., Subramanya, A., Patil, K., Pirsiavash, H.,
Role of Spatial Context in Adversarial Robustness for Object Detection,
AML-CV20(3403-3412)
IEEE DOI 2008
Detectors, Object detection, Cognition, Training, Blindness, Perturbation methods, Optimization BibRef

Jefferson, B., Marrero, C.O.,
Robust Assessment of Real-World Adversarial Examples,
AML-CV20(3442-3449)
IEEE DOI 2008
Cameras, Light emitting diodes, Robustness, Lighting, Detectors, Testing, Perturbation methods BibRef

Goel, A., Agarwal, A., Vatsa, M., Singh, R., Ratha, N.K.,
DNDNet: Reconfiguring CNN for Adversarial Robustness,
TCV20(103-110)
IEEE DOI 2008
Mathematical model, Perturbation methods, Machine learning, Robustness, Computational modeling, Databases BibRef

Cohen, G., Sapiro, G., Giryes, R.,
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors,
CVPR20(14441-14450)
IEEE DOI 2008
Training, Robustness, Loss measurement, Feature extraction, Neural networks, Perturbation methods, Training data BibRef

Dong, X., Han, J., Chen, D., Liu, J., Bian, H., Ma, Z., Li, H., Wang, X., Zhang, W., Yu, N.,
Robust Superpixel-Guided Attentional Adversarial Attack,
CVPR20(12892-12901)
IEEE DOI 2008
Perturbation methods, Robustness, Noise measurement, Image color analysis, Pipelines, Agriculture BibRef

Rahnama, A., Nguyen, A.T., Raff, E.,
Robust Design of Deep Neural Networks Against Adversarial Attacks Based on Lyapunov Theory,
CVPR20(8175-8184)
IEEE DOI 2008
Robustness, Nonlinear systems, Training, Control theory, Stability analysis, Perturbation methods, Transient analysis BibRef

Zhao, Y., Wu, Y., Chen, C., Lim, A.,
On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks,
CVPR20(1198-1207)
IEEE DOI 2008
Robustness, Data models, Solid modeling, Computational modeling, Perturbation methods BibRef

Gowal, S., Qin, C., Huang, P., Cemgil, T., Dvijotham, K., Mann, T., Kohli, P.,
Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations,
CVPR20(1208-1217)
IEEE DOI 2008
Perturbation methods, Robustness, Training, Semantics, Correlation, Task analysis, Mathematical model BibRef

Jeddi, A., Shafiee, M.J., Karg, M., Scharfenberger, C., Wong, A.,
Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness,
CVPR20(1238-1247)
IEEE DOI 2008
Perturbation methods, Robustness, Training, Neural networks, Data models, Uncertainty, Optimization BibRef

Dabouei, A.[Ali], Taherkhani, F.[Fariborz], Soleymani, S.[Sobhan], Nasrabadi, N.M.[Nasser M.],
Revisiting Outer Optimization in Adversarial Training,
ECCV22(V:244-261).
Springer DOI 2211
BibRef

Dabouei, A.[Ali], Soleymani, S.[Sobhan], Taherkhani, F.[Fariborz], Dawson, J., Nasrabadi, N.M.[Nasser M.],
Exploiting Joint Robustness to Adversarial Perturbations,
CVPR20(1119-1128)
IEEE DOI 2008
Robustness, Perturbation methods, Training, Predictive models, Optimization, Adaptation models BibRef

Addepalli, S.[Sravanti], Jain, S.[Samyak], Sriramanan, G.[Gaurang], Babu, R.V.[R. Venkatesh],
Scaling Adversarial Training to Large Perturbation Bounds,
ECCV22(V:301-316).
Springer DOI 2211
BibRef

Addepalli, S.[Sravanti], Vivek, B.S., Baburaj, A.[Arya], Sriramanan, G.[Gaurang], Babu, R.V.[R. Venkatesh],
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes,
CVPR20(1017-1026)
IEEE DOI 2008
Training, Robustness, Quantization (signal), Visual systems, Perturbation methods, Neural networks BibRef

Yuan, J., He, Z.,
Ensemble Generative Cleaning With Feedback Loops for Defending Adversarial Attacks,
CVPR20(578-587)
IEEE DOI 2008
Cleaning, Feedback loop, Transforms, Neural networks, Estimation, Fuses, Iterative methods BibRef

Guo, M., Yang, Y., Xu, R., Liu, Z., Lin, D.,
When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks,
CVPR20(628-637)
IEEE DOI 2008
Robustness, Training, Network architecture, Neural networks, Convolution, Architecture BibRef

Borkar, T., Heide, F., Karam, L.J.,
Defending Against Universal Attacks Through Selective Feature Regeneration,
CVPR20(706-716)
IEEE DOI 2008
Perturbation methods, Training, Robustness, Noise reduction, Image restoration, Transforms BibRef

Li, G., Ding, S., Luo, J., Liu, C.,
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder,
CVPR20(797-805)
IEEE DOI 2008
Noise reduction, Robustness, Training, Image restoration, Noise measurement, Decoding, Neural networks BibRef

Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., Wang, Z.,
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning,
CVPR20(696-705)
IEEE DOI 2008
Robustness, Task analysis, Training, Standards, Data models, Computational modeling, Tuning BibRef

Lee, S., Lee, H., Yoon, S.,
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization,
CVPR20(269-278)
IEEE DOI 2008
Robustness, Training, Standards, Perturbation methods, Complexity theory, Upper bound, Data models BibRef

Dong, Y., Fu, Q., Yang, X., Pang, T., Su, H., Xiao, Z., Zhu, J.,
Benchmarking Adversarial Robustness on Image Classification,
CVPR20(318-328)
IEEE DOI 2008
Robustness, Adaptation models, Training, Predictive models, Perturbation methods, Data models, Measurement BibRef

Xiao, C., Zheng, C.,
One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples,
CVPR20(409-418)
IEEE DOI 2008
Training, Robustness, Perturbation methods, Neural networks, Transforms, Mathematical model, Numerical models BibRef

Naseer, M., Khan, S., Hayat, M., Khan, F.S., Porikli, F.M.,
A Self-supervised Approach for Adversarial Robustness,
CVPR20(259-268)
IEEE DOI 2008
Perturbation methods, Task analysis, Distortion, Training, Robustness, Feature extraction, Neural networks BibRef

Zhao, Y., Tian, Y., Fowlkes, C., Shen, W., Yuille, A.L.,
Resisting Large Data Variations via Introspective Transformation Network,
WACV20(3069-3078)
IEEE DOI 2006
Training, Testing, Robustness, Training data, Linear programming, Resists BibRef

Kim, D.H.[Dong-Hyun], Bargal, S.A.[Sarah Adel], Zhang, J.M.[Jian-Ming], Sclaroff, S.[Stan],
Multi-way Encoding for Robustness,
WACV20(1341-1349)
IEEE DOI 2006
To counter adversarial attacks. Encoding, Robustness, Perturbation methods, Training, Biological system modeling, Neurons, Correlation BibRef

Folz, J., Palacio, S., Hees, J., Dengel, A.,
Adversarial Defense based on Structure-to-Signal Autoencoders,
WACV20(3568-3577)
IEEE DOI 2006
Perturbation methods, Semantics, Robustness, Predictive models, Training, Decoding, Neural networks BibRef

Zheng, S., Zhu, Z., Zhang, X., Liu, Z., Cheng, J., Zhao, Y.,
Distribution-Induced Bidirectional Generative Adversarial Network for Graph Representation Learning,
CVPR20(7222-7231)
IEEE DOI 2008
Generative adversarial networks, Robustness, Data models, Generators, Task analysis, Gaussian distribution BibRef

Vivek, B.S., Revanur, A.[Ambareesh], Venkat, N.[Naveen], Babu, R.V.[R. Venkatesh],
Plug-And-Pipeline: Efficient Regularization for Single-Step Adversarial Training,
TCV20(138-146)
IEEE DOI 2008
Training, Robustness, Computational modeling, Perturbation methods, Iterative methods, Backpropagation, Data models BibRef

Benz, P.[Philipp], Zhang, C.N.[Chao-Ning], Imtiaz, T.[Tooba], Kweon, I.S.[In So],
Double Targeted Universal Adversarial Perturbations,
ACCV20(IV:284-300).
Springer DOI 2103
BibRef
Earlier: A2,, A1, A3, A4:
Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations,
CVPR20(14509-14518)
IEEE DOI 2008
Perturbation methods, Correlation, Training data, Feature extraction, Training, Task analysis, Robustness BibRef

Zheng, H., Zhang, Z., Gu, J., Lee, H., Prakash, A.,
Efficient Adversarial Training With Transferable Adversarial Examples,
CVPR20(1178-1187)
IEEE DOI 2008
Training, Perturbation methods, Robustness, Computational modeling, Measurement, Iterative methods, Silicon BibRef

Shi, Y., Han, Y., Tian, Q.,
Polishing Decision-Based Adversarial Noise With a Customized Sampling,
CVPR20(1027-1035)
IEEE DOI 2008
Gaussian distribution, Sensitivity, Noise reduction, Optimization, Image coding, Robustness, Standards BibRef

Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A.L., Le, Q.V.,
Adversarial Examples Improve Image Recognition,
CVPR20(816-825)
IEEE DOI 2008
Training, Robustness, Degradation, Image recognition, Perturbation methods, Standards, Supervised learning BibRef

Dabouei, A., Soleymani, S., Taherkhani, F., Dawson, J., Nasrabadi, N.M.,
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations,
WACV20(2654-2663)
IEEE DOI 2006
Perturbation methods, Frequency-domain analysis, Robustness, Training, Optimization, Network architecture, Topology BibRef

Peterson, J.[Joshua], Battleday, R.[Ruairidh], Griffiths, T.[Thomas], Russakovsky, O.[Olga],
Human Uncertainty Makes Classification More Robust,
ICCV19(9616-9625)
IEEE DOI 2004
CIFAR10H dataset. To make deep network robust ot adversarial attacks. convolutional neural nets, learning (artificial intelligence), pattern classification, classification performance, Dogs BibRef

Wang, J., Zhang, H.,
Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks,
ICCV19(6628-6637)
IEEE DOI 2004
entropy, learning (artificial intelligence), neural nets, security of data, adversarial attacks, Data models BibRef

Ye, S., Xu, K., Liu, S., Cheng, H., Lambrechts, J., Zhang, H., Zhou, A., Ma, K., Wang, Y., Lin, X.,
Adversarial Robustness vs. Model Compression, or Both?,
ICCV19(111-120)
IEEE DOI 2004
minimax techniques, neural nets, security of data, adversarial attacks, concurrent adversarial training BibRef

Moosavi-Dezfooli, S.M.[Seyed-Mohsen], Fawzi, A.[Alhussein], Uesato, J.[Jonathan], Frossard, P.[Pascal],
Robustness via Curvature Regularization, and Vice Versa,
CVPR19(9070-9078).
IEEE DOI 2002
Adversarial training leads to more linear boundaries. BibRef

Xie, C.[Cihang], Wu, Y.X.[Yu-Xin], van der Maaten, L.[Laurens], Yuille, A.L.[Alan L.], He, K.M.[Kai-Ming],
Feature Denoising for Improving Adversarial Robustness,
CVPR19(501-509).
IEEE DOI 2002
BibRef

He, Z.[Zhezhi], Rakin, A.S.[Adnan Siraj], Fan, D.L.[De-Liang],
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack,
CVPR19(588-597).
IEEE DOI 2002
BibRef

Kaneko, T.[Takuhiro], Harada, T.[Tatsuya],
Blur, Noise, and Compression Robust Generative Adversarial Networks,
CVPR21(13574-13584)
IEEE DOI 2111
Degradation, Training, Adaptation models, Image coding, Uncertainty, Computational modeling BibRef

Kaneko, T.[Takuhiro], Harada, T.[Tatsuya],
Noise Robust Generative Adversarial Networks,
CVPR20(8401-8411)
IEEE DOI 2008
Training, Noise measurement, Generators, Noise robustness, Gaussian noise, Image generation BibRef

Kaneko, T.[Takuhiro], Ushiku, Y.[Yoshitaka], Harada, T.[Tatsuya],
Label-Noise Robust Generative Adversarial Networks,
CVPR19(2462-2471).
IEEE DOI 2002
BibRef

Miyazato, S., Wang, X., Yamasaki, T., Aizawa, K.,
Reinforcing the Robustness of a Deep Neural Network to Adversarial Examples by Using Color Quantization of Training Image Data,
ICIP19(884-888)
IEEE DOI 1910
convolutional neural network, adversarial example, color quantization BibRef

Ramanathan, T., Manimaran, A., You, S., Kuo, C.J.,
Robustness of Saak Transform Against Adversarial Attacks,
ICIP19(2531-2535)
IEEE DOI 1910
Saak transform, Adversarial attacks, Deep Neural Networks, Image Classification BibRef

Prakash, A., Moran, N., Garber, S., DiLillo, A., Storer, J.,
Deflecting Adversarial Attacks with Pixel Deflection,
CVPR18(8571-8580)
IEEE DOI 1812
Perturbation methods, Transforms, Minimization, Robustness, Noise reduction, Training BibRef

Mummadi, C.K., Brox, T., Metzen, J.H.,
Defending Against Universal Perturbations With Shared Adversarial Training,
ICCV19(4927-4936)
IEEE DOI 2004
image classification, image segmentation, neural nets, universal perturbations, shared adversarial training, Computational modeling BibRef

Chen, H., Liang, J., Chang, S., Pan, J., Chen, Y., Wei, W., Juan, D.,
Improving Adversarial Robustness via Guided Complement Entropy,
ICCV19(4880-4888)
IEEE DOI 2004
entropy, learning (artificial intelligence), neural nets, probability, adversarial defense, adversarial robustness, BibRef

Bai, Y., Feng, Y., Wang, Y., Dai, T., Xia, S., Jiang, Y.,
Hilbert-Based Generative Defense for Adversarial Examples,
ICCV19(4783-4792)
IEEE DOI 2004
feature extraction, Hilbert transforms, neural nets, security of data, scan mode, advanced Hilbert curve scan order BibRef

Jang, Y., Zhao, T., Hong, S., Lee, H.,
Adversarial Defense via Learning to Generate Diverse Attacks,
ICCV19(2740-2749)
IEEE DOI 2004
learning (artificial intelligence), neural nets, pattern classification, security of data, adversarial defense, Machine learning BibRef

Mustafa, A., Khan, S., Hayat, M., Goecke, R., Shen, J., Shao, L.,
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks,
ICCV19(3384-3393)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, image representation, Iterative methods BibRef

Taran, O.[Olga], Rezaeifar, S.[Shideh], Holotyak, T.[Taras], Voloshynovskiy, S.[Slava],
Defending Against Adversarial Attacks by Randomized Diversification,
CVPR19(11218-11225).
IEEE DOI 2002
BibRef

Sun, B.[Bo], Tsai, N.H.[Nian-Hsuan], Liu, F.C.[Fang-Chen], Yu, R.[Ronald], Su, H.[Hao],
Adversarial Defense by Stratified Convolutional Sparse Coding,
CVPR19(11439-11448).
IEEE DOI 2002
BibRef

Ho, C.H.[Chih-Hui], Leung, B.[Brandon], Sandstrom, E.[Erik], Chang, Y.[Yen], Vasconcelos, N.M.[Nuno M.],
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks,
CVPR19(9221-9229).
IEEE DOI 2002
BibRef

Dubey, A.[Abhimanyu], van der Maaten, L.[Laurens], Yalniz, Z.[Zeki], Li, Y.X.[Yi-Xuan], Mahajan, D.[Dhruv],
Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search,
CVPR19(8759-8768).
IEEE DOI 2002
BibRef

Dong, Y.P.[Yin-Peng], Pang, T.Y.[Tian-Yu], Su, H.[Hang], Zhu, J.[Jun],
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks,
CVPR19(4307-4316).
IEEE DOI 2002
BibRef

Rony, J.[Jerome], Hafemann, L.G.[Luiz G.], Oliveira, L.S.[Luiz S.], Ben Ayed, I.[Ismail], Sabourin, R.[Robert], Granger, E.[Eric],
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses,
CVPR19(4317-4325).
IEEE DOI 2002
BibRef

Qiu, Y.X.[Yu-Xian], Leng, J.W.[Jing-Wen], Guo, C.[Cong], Chen, Q.[Quan], Li, C.[Chao], Guo, M.[Minyi], Zhu, Y.H.[Yu-Hao],
Adversarial Defense Through Network Profiling Based Path Extraction,
CVPR19(4772-4781).
IEEE DOI 2002
BibRef

Jia, X.J.[Xiao-Jun], Wei, X.X.[Xing-Xing], Cao, X.C.[Xiao-Chun], Foroosh, H.[Hassan],
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples,
CVPR19(6077-6085).
IEEE DOI 2002
BibRef

Raff, E.[Edward], Sylvester, J.[Jared], Forsyth, S.[Steven], McLean, M.[Mark],
Barrage of Random Transforms for Adversarially Robust Defense,
CVPR19(6521-6530).
IEEE DOI 2002
BibRef

Ji, J., Zhong, B., Ma, K.,
Multi-Scale Defense of Adversarial Images,
ICIP19(4070-4074)
IEEE DOI 1910
deep learning, adversarial images, defense, multi-scale, image evolution BibRef

Agarwal, C., Nguyen, A., Schonfeld, D.,
Improving Robustness to Adversarial Examples by Encouraging Discriminative Features,
ICIP19(3801-3805)
IEEE DOI 1910
Adversarial Machine Learning, Robustness, Defenses, Deep Learning BibRef

Saha, S., Kumar, A., Sahay, P., Jose, G., Kruthiventi, S., Muralidhara, H.,
Attack Agnostic Statistical Method for Adversarial Detection,
SDL-CV19(798-802)
IEEE DOI 2004
feature extraction, image classification, learning (artificial intelligence), neural nets, Adversarial Attack BibRef

Taran, O.[Olga], Rezaeifar, S.[Shideh], Voloshynovskiy, S.[Slava],
Bridging Machine Learning and Cryptography in Defence Against Adversarial Attacks,
Objectionable18(II:267-279).
Springer DOI 1905
BibRef

Naseer, M., Khan, S., Porikli, F.M.,
Local Gradients Smoothing: Defense Against Localized Adversarial Attacks,
WACV19(1300-1307)
IEEE DOI 1904
data compression, feature extraction, gradient methods, image classification, image coding, image representation, High frequency BibRef

Akhtar, N., Liu, J., Mian, A.,
Defense Against Universal Adversarial Perturbations,
CVPR18(3389-3398)
IEEE DOI 1812
Perturbation methods, Training, Computational modeling, Detectors, Neural networks, Robustness, Integrated circuits BibRef

Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., Zhu, J.,
Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser,
CVPR18(1778-1787)
IEEE DOI 1812
Training, Perturbation methods, Noise reduction, Image reconstruction, Predictive models, Neural networks, Adaptation models BibRef

Behpour, S., Xing, W., Ziebart, B.D.,
ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-label Classification,
WiCV18(1986-19862)
IEEE DOI 1812
Markov random fields, Task analysis, Training, Testing, Support vector machines, Fasteners, Games BibRef

Karim, R., Islam, M.A., Mohammed, N., Bruce, N.D.B.,
On the Robustness of Deep Learning Models to Universal Adversarial Attack,
CRV18(55-62)
IEEE DOI 1812
Perturbation methods, Computational modeling, Neural networks, Task analysis, Image segmentation, Data models, Semantics, Semantic Segmentation BibRef

Jakubovitz, D.[Daniel], Giryes, R.[Raja],
Improving DNN Robustness to Adversarial Attacks Using Jacobian Regularization,
ECCV18(XII: 525-541).
Springer DOI 1810
BibRef

Rozsa, A., Gunther, M., Boult, T.E.,
Towards Robust Deep Neural Networks with BANG,
WACV18(803-811)
IEEE DOI 1806
image processing, learning (artificial intelligence), neural nets, BANG technique, adversarial image utilization, Training BibRef

Lu, J., Issaranon, T., Forsyth, D.A.,
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly,
ICCV17(446-454)
IEEE DOI 1802
image colour analysis, image reconstruction, learning (artificial intelligence), neural nets, BibRef

Mukuta, Y., Ushiku, Y., Harada, T.,
Spatial-Temporal Weighted Pyramid Using Spatial Orthogonal Pooling,
CEFR-LCV17(1041-1049)
IEEE DOI 1802
Encoding, Feature extraction, Robustness, Spatial resolution, Standards BibRef

Moosavi-Dezfooli, S.M.[Seyed-Mohsen], Fawzi, A.[Alhussein], Fawzi, O.[Omar], Frossard, P.[Pascal],
Universal Adversarial Perturbations,
CVPR17(86-94)
IEEE DOI 1711
Correlation, Neural networks, Optimization, Robustness, Training, Visualization BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Adversarial Attacks .