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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
Shi, Y.C.[Yu-Cheng],
Han, Y.H.[Ya-Hong],
Zhang, Q.X.[Quan-Xin],
Kuang, X.H.[Xiao-Hui],
Adaptive iterative attack towards explainable adversarial robustness,
PR(105), 2020, pp. 107309.
Elsevier DOI
2006
Adversarial example, Adversarial attack, Image classification
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Wang, Y.,
Su, H.,
Zhang, B.,
Hu, X.,
Interpret Neural Networks by Extracting Critical Subnetworks,
IP(29), 2020, pp. 6707-6720.
IEEE DOI
2007
Predictive models, Logic gates, Neural networks, Machine learning,
Feature extraction, Robustness, Visualization, adversarial robustness
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Ortiz-Jiménez, G.[Guillermo],
Modas, A.[Apostolos],
Moosavi-Dezfooli, S.M.[Seyed-Mohsen],
Frossard, P.[Pascal],
Optimism in the Face of Adversity: Understanding and Improving Deep
Learning Through Adversarial Robustness,
PIEEE(109), No. 5, May 2021, pp. 635-659.
IEEE DOI
2105
Neural networks, Deep learning, Robustness, Security, Tools,
Perturbation methods, Benchmark testing, Adversarial robustness,
transfer learning
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
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
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
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
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
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, 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
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
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
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
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
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
Li, W.[Wen],
Wang, H.Y.[Heng-You],
Huo, L.Z.[Lian-Zhi],
He, Q.[Qiang],
Zhang, C.L.[Chang-Lun],
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
Dong, J.H.[Jun-Hao],
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
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
Heo, J.[Jaehyuk],
Seo, S.[Seungwan],
Kang, P.[Pilsung],
Exploring the differences in adversarial robustness between ViT- and
CNN-based models using novel metrics,
CVIU(235), 2023, pp. 103800.
Elsevier DOI
2310
Adversarial robustness, Computer vision
BibRef
Wang, K.[Ke],
Chen, Z.C.[Zi-Cong],
Dang, X.L.[Xi-Lin],
Fan, X.[Xuan],
Han, X.M.[Xu-Ming],
Chen, C.M.[Chien-Ming],
Ding, W.P.[Wei-Ping],
Yiu, S.M.[Siu-Ming],
Weng, J.[Jian],
Uncovering Hidden Vulnerabilities in Convolutional Neural Networks
through Graph-based Adversarial Robustness Evaluation,
PR(143), 2023, pp. 109745.
Elsevier DOI
2310
Graph of patterns, Graph distance algorithm,
Adversarial robustness, Interpretable graph-based systems,
Convolutional neural networks
BibRef
Chu, T.S.[Tian-Shu],
Fang, K.[Kun],
Yang, J.[Jie],
Huang, X.L.[Xiao-Lin],
Improving the adversarial robustness of quantized neural networks via
exploiting the feature diversity,
PRL(176), 2023, pp. 117-122.
Elsevier DOI
2312
Quantized neural networks, Adversarial robustness,
Orthogonal regularization, Feature diversity
BibRef
Fang, K.[Kun],
Tao, Q.H.[Qing-Hua],
Wu, Y.W.[Ying-Wen],
Li, T.[Tao],
Cai, J.[Jia],
Cai, F.P.[Fei-Peng],
Huang, X.L.[Xiao-Lin],
Yang, J.[Jie],
Towards robust neural networks via orthogonal diversity,
PR(149), 2024, pp. 110281.
Elsevier DOI
2403
Model augmentation, Multi-head, Orthogonality,
Margin-maximization, Data augmentation, Adversarial 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
Xu, S.W.[Sheng-Wang],
Qiao, T.[Tong],
Xu, M.[Ming],
Wang, W.[Wei],
Zheng, N.[Ning],
Robust Adversarial Watermark Defending Against GAN Synthesization
Attack,
SPLetters(31), 2024, pp. 351-355.
IEEE DOI
2402
Watermarking, Transform coding, Generative adversarial networks, Forgery,
Image coding, Discrete cosine transforms, Decoding, JPEG compression
BibRef
Zhuang, W.[Wenzi],
Huang, L.F.[Li-Feng],
Gao, C.Y.[Cheng-Ying],
Liu, N.[Ning],
LAFED: Towards robust ensemble models via Latent Feature
Diversification,
PR(150), 2024, pp. 110225.
Elsevier DOI Code:
WWW Link.
2403
Adversarial example, Adversarial defense, Ensemble model, Robustness
BibRef
Zhang, L.[Lei],
Zhou, Y.H.[Yu-Hang],
Yang, Y.[Yi],
Gao, X.B.[Xin-Bo],
Meta Invariance Defense Towards Generalizable Robustness to Unknown
Adversarial Attacks,
PAMI(46), No. 10, October 2024, pp. 6669-6687.
IEEE DOI
2409
Robustness, Training, Task analysis, Feature extraction,
Metalearning, Perturbation methods, Artificial neural networks,
deep neural network
BibRef
Li, Z.R.[Zhuo-Rong],
Wu, M.H.[Ming-Hui],
Jin, C.[Canghong],
Yu, D.[Daiwei],
Yu, H.[Hongchuan],
Adversarial self-training for robustness and generalization,
PRL(185), 2024, pp. 117-123.
Elsevier DOI
2410
Adversarial defense, Adversarial attack, Robustness,
Generalization, Self-training
BibRef
Li, Z.Y.[Ze-Yang],
Hu, C.[Chuxiong],
Wang, Y.[Yunan],
Yang, Y.J.[Yu-Jie],
Li, S.B.E.[Sheng-Bo Eben],
Safe Reinforcement Learning With Dual Robustness,
PAMI(46), No. 12, December 2024, pp. 10876-10890.
IEEE DOI
2411
Safety, Games, Game theory, Task analysis, Robustness, Optimization,
Convergence, Reinforcement learning, robustness, safety, zero-sum Markov game
BibRef
Li, J.W.[Jia-Wen],
Fang, K.[Kun],
Huang, X.L.[Xiao-Lin],
Yang, J.[Jie],
Boosting certified robustness via an expectation-based similarity
regularization,
IVC(151), 2024, pp. 105272.
Elsevier DOI
2411
Image classification, Adversarial robustness, Metric learning,
Certified robustness, Randomized smoothing
BibRef
Xiao, J.C.[Jian-Cong],
Yang, L.[Liusha],
Fan, Y.B.[Yan-Bo],
Wang, J.[Jue],
Luo, Z.Q.[Zhi-Quan],
Understanding adversarial robustness against on-manifold adversarial
examples,
PR(159), 2025, pp. 111071.
Elsevier DOI
2412
Adversarial robustness, On-manifold adversarial examples
BibRef
Li, L.[Lin],
Qiu, J.N.[Jia-Ning],
Spratling, M.W.[Michael W.],
AROID: Improving Adversarial Robustness Through Online Instance-Wise
Data Augmentation,
IJCV(133), No. 2, February 2025, pp. 929-950.
Springer DOI
2502
BibRef
Fu, X.W.[Xiao-Wei],
Ma, L.[Lina],
Zhang, L.[Lei],
Remove to Regenerate: Boosting Adversarial Generalization with Attack
Invariance,
CirSysVideo(35), No. 3, March 2025, pp. 1999-2012.
IEEE DOI Code:
WWW Link.
2503
Robustness, Perturbation methods, Semantics, Surgery,
Medical treatment, Cancer, Lesions, Accuracy, Malignant tumors,
semantic regeneration
BibRef
Dardour, O.[Omar],
Aguilar, E.[Eduardo],
Radeva, P.[Petia],
Zaied, M.[Mourad],
Inter-separability and intra-concentration to enhance stochastic
neural network adversarial robustness,
PRL(191), 2025, pp. 1-7.
Elsevier DOI
2504
Deep neural networks, Adversarial robustness, Uncertainty,
Label embedding, Inter-separability, Intra-compactness
BibRef
Peng, H.Q.[He-Qi],
Chen, M.X.[Ming-Xuan],
Wang, Y.H.[Yun-Hong],
Guo, Y.F.[Yuan-Fang],
HFA2RE: Enhancing adversarial robustness via Hyperspherical Feature
Aggregation,
PR(169), 2026, pp. 111857.
Elsevier DOI
2509
Adversarial training, Self-supervised learning, Adversarial robustness
BibRef
Jeary, L.[Linus],
Kuipers, T.[Tom],
Hosseini, M.[Mehran],
Paoletti, N.[Nicola],
Verifiably robust conformal prediction for probabilistic guarantees
under adversarial attacks,
PR(170), 2026, pp. 112051.
Elsevier DOI
2509
Formal verification, Conformal prediction,
Robust conformal prediction, Adversarial robustness, Poisoning attacks
BibRef
Wang, Z.[Zheng],
Xu, X.[Xing],
Zhu, L.[Lei],
Bin, Y.[Yi],
Wang, G.Q.[Guo-Qing],
Yang, Y.[Yang],
Shen, H.T.[Heng Tao],
Evidence-Based Multi-Feature Fusion for Adversarial Robustness,
PAMI(47), No. 10, October 2025, pp. 8923-8937.
IEEE DOI
2510
Robustness, Training, Perturbation methods,
Representation learning, Uncertainty, Transformers, Optimization,
evidential deep learning
BibRef
Yang, X.[Xiao],
Wu, L.X.[Ling-Xuan],
Wang, L.Z.[Li-Zhong],
Ying, C.Y.[Cheng-Yang],
Su, H.[Hang],
Zhu, J.[Jun],
Reinforced Embodied Active Defense: Exploiting Adaptive Interaction
for Robust Visual Perception in Adversarial 3D Environments,
PAMI(47), No. 10, October 2025, pp. 9078-9094.
IEEE DOI
2510
Training, Robustness, Visual perception, Perturbation methods,
Autonomous vehicles, Artificial neural networks,
policy learning
BibRef
Xie, Y.[Yong],
Zheng, W.J.[Wei-Jie],
Huang, H.[Hanxun],
Ye, G.[Guangnan],
Ma, X.[Xingjun],
Towards Million-Scale Adversarial Robustness Evaluation With Stronger
Individual Attacks,
CVPR25(30702-30711)
IEEE DOI
2508
Deep learning, Perturbation methods, Robustness, Distance measurement,
Ensemble learning, Glass box, Testing, Image classification
BibRef
Shen, H.[Huakun],
Hu, B.Y.C.[Bo-Yue Caroline],
Czarnecki, K.[Krzysztof],
Marsso, L.[Lina],
Chechik, M.[Marsha],
Assessing Visually-Continuous Corruption Robustness of Neural
Networks Relative to Human Performance,
WACV25(6300-6310)
IEEE DOI
2505
Measurement, Visualization, Accuracy, Training data,
Artificial neural networks, Benchmark testing, Transformers, VCR
BibRef
Rodríguez-Muñoz, A.[Adrián],
Wang, T.Z.[Tong-Zhou],
Torralba, A.[Antonio],
Characterizing Model Robustness via Natural Input Gradients,
ECCV24(LXXVI: 161-178).
Springer DOI
2412
BibRef
Pulfer, B.[Brian],
Belousov, Y.[Yury],
Voloshynovskiy, S.[Slava],
Robustness Tokens: Towards Adversarial Robustness of Transformers,
ECCV24(LIX: 110-127).
Springer DOI
2412
BibRef
Dibbo, S.V.[Sayanton V.],
Breuer, A.[Adam],
Moore, J.[Juston],
Teti, M.[Michael],
Improving Robustness to Model Inversion Attacks via Sparse Coding
Architectures,
ECCV24(LXXX: 117-136).
Springer DOI
2412
BibRef
Zhang, C.S.[Chen-Shuang],
Pan, F.[Fei],
Kim, J.[Junmo],
Kweon, I.S.[In So],
Mao, C.Z.[Cheng-Zhi],
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion
Synthetic Object,
CVPR24(21752-21762)
IEEE DOI Code:
WWW Link.
2410
Visualization, Accuracy, Computational modeling, Soft sensors,
Benchmark testing, Diffusion models, Robustness, Dataset
BibRef
Franco, N.[Nicola],
Lorenz, J.M.[Jeanette Miriam],
Roscher, K.[Karsten],
Günnemann, S.[Stephan],
Understanding ReLU Network Robustness Through Test Set Certification
Performance,
SAIAD24(3451-3460)
IEEE DOI
2410
Accuracy, Perturbation methods, Neural networks, Reliability theory,
Robustness, Stability analysis, Safety, Formal Verification
BibRef
Cui, X.M.[Xuan-Ming],
Aparcedo, A.[Alejandro],
Jang, Y.K.[Young Kyun],
Lim, S.N.[Ser-Nam],
On the Robustness of Large Multimodal Models Against Image
Adversarial Attacks,
CVPR24(24625-24634)
IEEE DOI
2410
Visualization, Accuracy, Robustness,
Question answering (information retrieval),
Adversarial attack
BibRef
Christensen, P.E.[Peter Ebert],
Snæbjarnarson, V.[Vésteinn],
Dittadi, A.[Andrea],
Belongie, S.[Serge],
Benaim, S.[Sagie],
Assessing Neural Network Robustness via Adversarial Pivotal Tuning,
WACV24(2940-2949)
IEEE DOI
2404
Training, Semantics, Neural networks, Training data,
Benchmark testing, Robustness, Generators, Algorithms
BibRef
Cohen, G.[Gilad],
Giryes, R.[Raja],
Simple Post-Training Robustness using Test Time Augmentations and
Random Forest,
WACV24(3984-3994)
IEEE DOI Code:
WWW Link.
2404
Training, Threat modeling, Adaptation models, Image color analysis,
Artificial neural networks, Transforms, Robustness, Algorithms,
adversarial attack and defense methods
BibRef
Sharma, A.[Abhijith],
Munz, P.[Phil],
Narayan, A.[Apurva],
Assist Is Just as Important as the Goal:
Image Resurfacing to Aid Model's Robust Prediction,
WACV24(3821-3830)
IEEE DOI
2404
Visualization, TV, Perturbation methods, Predictive models,
Benchmark testing, Security, Algorithms, Adversarial learning,
adversarial attack and defense methods
BibRef
Schlarmann, C.[Christian],
Hein, M.[Matthias],
On the Adversarial Robustness of Multi-Modal Foundation Models,
AROW23(3679-3687)
IEEE DOI
2401
BibRef
Yang, D.Y.[Dong-Yoon],
Kong, I.[Insung],
Kim, Y.[Yongdai],
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively
Weighted Regularization and Knowledge Distillation,
ICCV23(4529-4538)
IEEE DOI
2401
BibRef
Suzuki, S.[Satoshi],
Yamaguchi, S.[Shin'ya],
Takeda, S.[Shoichiro],
Kanai, S.[Sekitoshi],
Makishima, N.[Naoki],
Ando, A.[Atsushi],
Masumura, R.[Ryo],
Adversarial Finetuning with Latent Representation Constraint to
Mitigate Accuracy-Robustness Tradeoff,
ICCV23(4367-4378)
IEEE DOI
2401
BibRef
Ji, Q.F.[Qiu-Fan],
Wang, L.[Lin],
Shi, C.[Cong],
Hu, S.S.[Sheng-Shan],
Chen, Y.Y.[Ying-Ying],
Sun, L.C.[Li-Chao],
Benchmarking and Analyzing Robust Point Cloud Recognition:
Bag of Tricks for Defending Adversarial Examples,
ICCV23(4272-4281)
IEEE DOI Code:
WWW Link.
2401
BibRef
Jin, Y.L.[Yu-Lin],
Zhang, X.Y.[Xiao-Yu],
Lou, J.[Jian],
Ma, X.[Xu],
Wang, Z.L.[Zi-Long],
Chen, X.F.[Xiao-Feng],
Explaining Adversarial Robustness of Neural Networks from Clustering
Effect Perspective,
ICCV23(4499-4508)
IEEE DOI Code:
WWW Link.
2401
BibRef
Li, Y.M.[Yi-Ming],
Fang, Q.[Qi],
Bai, J.[Jiamu],
Chen, S.[Siheng],
Xu, F.J.F.[Felix Jue-Fei],
Feng, C.[Chen],
Among Us: Adversarially Robust Collaborative Perception by Consensus,
ICCV23(186-195)
IEEE DOI
2401
BibRef
Lee, M.J.[Min-Jong],
Kim, D.[Dongwoo],
Robust Evaluation of Diffusion-Based Adversarial Purification,
ICCV23(134-144)
IEEE DOI
2401
Evaluation of purification process at run-time.
BibRef
Sharma, S.[Shivam],
Joshi, R.[Rohan],
Bhilare, S.[Shruti],
Joshi, M.V.[Manjunath V.],
Robust Adversarial Defence: Use of Auto-inpainting,
CAIP23(I:110-119).
Springer DOI
2312
BibRef
Piat, W.[William],
Fadili, J.[Jalal],
Jurie, S.F.[S Frédéric],
Exploring the Connection Between Neuron Coverage and Adversarial
Robustness in DNN Classifiers,
ICIP23(745-749)
IEEE DOI
2312
BibRef
Atsague, M.[Modeste],
Nirala, A.[Ashutosh],
Fakorede, O.[Olukorede],
Tian, J.[Jin],
A Penalized Modified Huber Regularization to Improve Adversarial
Robustness,
ICIP23(2675-2679)
IEEE DOI
2312
BibRef
Wang, B.H.[Bing-Hui],
Pang, M.[Meng],
Dong, Y.[Yun],
Turning Strengths into Weaknesses: A Certified Robustness Inspired
Attack Framework against Graph Neural Networks,
CVPR23(16394-16403)
IEEE DOI
2309
BibRef
Huang, B.[Bo],
Chen, M.Y.[Ming-Yang],
Wang, Y.[Yi],
Lu, J.[Junda],
Cheng, M.[Minhao],
Wang, W.[Wei],
Boosting Accuracy and Robustness of Student Models via Adaptive
Adversarial Distillation,
CVPR23(24668-24677)
IEEE DOI
2309
BibRef
Dong, M.J.[Min-Jing],
Xu, C.[Chang],
Adversarial Robustness via Random Projection Filters,
CVPR23(4077-4086)
IEEE DOI
2309
BibRef
Kim, W.J.[Woo Jae],
Cho, Y.[Yoonki],
Jung, J.[Junsik],
Yoon, S.E.[Sung-Eui],
Feature Separation and Recalibration for Adversarial Robustness,
CVPR23(8183-8192)
IEEE DOI
2309
BibRef
Huang, S.H.[Shi-Hua],
Lu, Z.C.[Zhi-Chao],
Deb, K.[Kalyanmoy],
Boddeti, V.N.[Vishnu Naresh],
Revisiting Residual Networks for Adversarial Robustness,
CVPR23(8202-8211)
IEEE DOI
2309
BibRef
Kim, J.[Junho],
Lee, B.K.[Byung-Kwan],
Ro, Y.M.[Yong Man],
Demystifying Causal Features on Adversarial Examples and Causal
Inoculation for Robust Network by Adversarial Instrumental Variable
Regression,
CVPR23(12032-12042)
IEEE DOI
2309
BibRef
Croce, F.[Francesco],
Rebuffi, S.A.[Sylvestre-Alvise],
Shelhamer, E.[Evan],
Gowal, S.[Sven],
Seasoning Model Soups for Robustness to Adversarial and Natural
Distribution Shifts,
CVPR23(12313-12323)
IEEE DOI
2309
BibRef
Li, Z.W.[Zhuo-Wan],
Wong, X.R.[Xing-Rui],
Stengel-Eskin, E.[Elias],
Kortylewski, A.[Adam],
Ma, W.[Wufei],
van Durme, B.[Benjamin],
Yuille, A.L.[Alan L.],
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in
Visual Reasoning,
CVPR23(14963-14973)
IEEE DOI
2309
BibRef
Wang, Z.[Zifan],
Ding, N.[Nan],
Levinboim, T.[Tomer],
Chen, X.[Xi],
Soricut, R.[Radu],
Improving Robust Generalization by Direct PAC-Bayesian Bound
Minimization,
CVPR23(16458-16468)
IEEE DOI
2309
BibRef
Agarwal, A.[Akshay],
Ratha, N.[Nalini],
Singh, R.[Richa],
Vatsa, M.[Mayank],
Robustness Against Gradient based Attacks through Cost Effective
Network Fine-Tuning,
FaDE-TCV23(28-37)
IEEE DOI
2309
BibRef
Liang, H.Y.[Heng-Yue],
Liang, B.[Buyun],
Sun, J.[Ju],
Cui, Y.[Ying],
Mitchell, T.[Tim],
Implications of Solution Patterns on Adversarial Robustness,
AML23(2393-2400)
IEEE DOI
2309
BibRef
Redgrave, T.[Timothy],
Crum, C.[Colton],
Generating Adversarial Samples in Mini-Batches May Be Detrimental To
Adversarial Robustness,
AML23(2378-2384)
IEEE DOI
2309
BibRef
Gavrikov, P.[Paul],
Keuper, J.[Janis],
On the Interplay of Convolutional Padding and Adversarial Robustness,
BRAVO23(3983-3992)
IEEE DOI
2401
BibRef
Wang, R.[Ren],
Li, Y.X.[Yu-Xuan],
Liu, S.[Sijia],
Exploring Diversified Adversarial Robustness in Neural Networks via
Robust Mode Connectivity,
AML23(2346-2352)
IEEE DOI
2309
BibRef
Nandi, S.[Soumalya],
Addepalli, S.[Sravanti],
Rangwani, H.[Harsh],
Babu, R.V.[R. Venkatesh],
Certified Adversarial Robustness Within Multiple Perturbation Bounds,
AML23(2298-2305)
IEEE DOI
2309
BibRef
Sarkar, S.[Soumyendu],
Babu, A.R.[Ashwin Ramesh],
Mousavi, S.[Sajad],
Ghorbanpour, S.[Sahand],
Gundecha, V.[Vineet],
Guillen, A.[Antonio],
Luna, R.[Ricardo],
Naug, A.[Avisek],
Robustness with Query-efficient Adversarial Attack using
Reinforcement Learning,
AML23(2330-2337)
IEEE DOI
2309
BibRef
Mofayezi, M.[Mohammadreza],
Medghalchi, Y.[Yasamin],
Benchmarking Robustness to Text-Guided Corruptions,
GCV23(779-786)
IEEE DOI
2309
BibRef
Chandna, K.[Kshitij],
Improving Adversarial Robustness by Penalizing Natural Accuracy,
AdvRob22(517-533).
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
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
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
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
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
Li, K.D.[Kai-Dong],
Zhang, Z.M.[Zi-Ming],
Zhong, C.C.[Cun-Cong],
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,
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
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
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
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
Mao, X.F.[Xiao-Feng],
Qi, G.[Gege],
Chen, Y.F.[Yue-Feng],
Li, X.D.[Xiao-Dan],
Duan, R.J.[Ran-Jie],
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
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
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
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
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
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
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
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
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
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
Wu, T.[Tong],
Liu, Z.W.[Zi-Wei],
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Adversarial Patch Attacks, Spatial Context, Defense .