25.3.10.1 Forgery Detection for Images

Chapter Contents (Back)
Forgery Detection. Specifically copy-move:
See also Copy-Move Tamper Detection, Forensics.
See also Double Compression, Double JPEG Detection, Forensics.
See also Deepfakes, Face Synthesis, Fake News, Generation, Detection.

Mahdian, B.[Babak], Saic, S.[Stanislav],
Using noise inconsistencies for blind image forensics,
IVC(27), No. 10, 2 September 2009, pp. 1497-1503.
Elsevier DOI 0906
Image forensics; Digital forgery; Image tampering; Image segmentation; Noise inconsistency BibRef

Mahdian, B.[Babak], Saic, S.[Stanislav],
A bibliography on blind methods for identifying image forgery,
SP:IC(25), No. 6, July 2010, pp. 389-399.
Elsevier DOI 1007
Survey, Image Forensics. Survey, Forgery Detecion. Image forensics; Digital forgery; Image tampering; Blind forgery detection; Multimedia security BibRef

Mahdian, B.[Babak], Saic, S.[Stanislav],
A cyclostationarity analysis applied to image forensics,
WACV09(1-6).
IEEE DOI 0912
BibRef

Yerushalmy, I.[Ido], Hel-Or, H.[Hagit],
Digital Image Forgery Detection Based on Lens and Sensor Aberration,
IJCV(92), No. 1, March 2011, pp. 71-91.
WWW Link. 1103
BibRef

Lin, G.S.[Guo-Shiang], Chang, M.K.[Min-Kuan], Chen, Y.L.[You-Lin],
A Passive-Blind Forgery Detection Scheme Based on Content-Adaptive Quantization Table Estimation,
CirSysVideo(21), No. 4, April 2011, pp. 421-434.
IEEE DOI 1104
BibRef
Earlier:
Image Forgery Detection Based on Quantization Table Estimation,
MVA09(66-).
PDF File. 0905
BibRef

Yao, H.[Heng], Wang, S.Z.[Shuo-Zhong], Zhao, Y.[Yan], Zhang, X.P.[Xin-Peng],
Detecting Image Forgery Using Perspective Constraints,
SPLetters(19), No. 3, March 2012, pp. 123-126.
IEEE DOI 1202
BibRef

Qazi, T., Hayat, K., Khan, S.U., Madani, S.A., Khan, I.A., Kolodziej, J., Li, H., Lin, W., Yow, K.C., Xu, C.Z.,
Survey on blind image forgery detection,
IET-IPR(7), No. 7, October 2013, pp. 660-670.
DOI Link 1312
Survey, Forgery Detection. image forensics BibRef

Qureshi, M.A.[Muhammad Ali], Deriche, M.[Mohamed],
A bibliography of pixel-based blind image forgery detection techniques,
SP:IC(39, Part A), No. 1, 2015, pp. 46-74.
Elsevier DOI 1512
Image forensics BibRef

Li, Y.M.[Yuan-Man], Zhou, J.T.[Jian-Tao],
Anti-Forensics of Lossy Predictive Image Compression,
SPLetters(22), No. 12, December 2015, pp. 2219-2223.
IEEE DOI 1512
data compression. Compression can remove protections. BibRef

Pun, C.M.[Chi-Man], Liu, B.[Bo], Yuan, X.C.[Xiao-Chen],
Multi-scale noise estimation for image splicing forgery detection,
JVCIR(38), No. 1, 2016, pp. 195-206.
Elsevier DOI 1605
Splicing forgery BibRef

Emam, M.[Mahmoud], Han, Q.[Qi], Yu, L.Y.[Li-Yang], Zhang, H.L.[Hong-Li],
A Keypoint-Based Region Duplication Forgery Detection Algorithm,
IEICE(E99-D), No. 9, September 2016, pp. 2413-2416.
WWW Link. 1609
BibRef

Chen, S., Tan, S., Li, B., Huang, J.,
Automatic Detection of Object-Based Forgery in Advanced Video,
CirSysVideo(26), No. 11, November 2016, pp. 2138-2151.
IEEE DOI 1609
Detectors BibRef

Salloum, R.[Ronald], Ren, Y.Z.[Yu-Zhuo], Kuo, C.C.J.[C.C. Jay],
Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN),
JVCIR(51), 2018, pp. 201-209.
Elsevier DOI 1802
Image splicing, Image forensics, Convolutional neural network (CNN), Multi-task network BibRef

Shein, E.[Esther],
The State of Fakery,
CACM(61), No. 3, March 2018, pp. 21-23.
DOI Link 1804
Authenticate digital media BibRef

Ding, X., Yang, G., Li, R., Zhang, L., Li, Y., Sun, X.,
Identification of Motion-Compensated Frame Rate Up-Conversion Based on Residual Signals,
CirSysVideo(28), No. 7, July 2018, pp. 1497-1512.
IEEE DOI 1807
Bit rate, Forensics, Forgery, Image edge detection, Markov processes, Videos, Blind video forensics, residual signal and classification BibRef

Cristin, R.[Rajan], Ananth, J.P.[John Patrick], Raj, V.C.[Velankanni Cyril],
Illumination-based texture descriptor and fruitfly support vector neural network for image forgery detection in face images,
IET-IPR(12), No. 8, August 2018, pp. 1439-1449.
DOI Link 1808
BibRef

Verma, V.[Vinay], Agarwal, N.[Nikita], Khanna, N.[Nitin],
DCT-domain deep convolutional neural networks for multiple JPEG compression classification,
SP:IC(67), 2018, pp. 22-33.
Elsevier DOI 1808
Image forensics, Compression forensics, Deep convolutional neural network (CNN), JPEG forensics, Forgery detection BibRef

Ehret, T.[Thibaud],
Automatic Detection of Internal Copy-Move Forgeries in Images,
IPOL(8), 2018, pp. 167-191.
DOI Link 1808
Code, Forgery Detection. BibRef

He, P.S.[Pei-Song], Jiang, X.H.[Xing-Hao], Sun, T.F.[Tan-Feng], Li, H.L.[Hao-Liang],
Computer Graphics Identification Combining Convolutional and Recurrent Neural Networks,
SPLetters(25), No. 9, September 2018, pp. 1369-1373.
IEEE DOI 1809
Distinguish photographs of reality from computer graphics. directed graphs, feature extraction, image classification, image colour analysis, image representation, image texture, recurrent neural network (RNN) BibRef

Nasiri, M.[Morteza], Behrad, A.[Alireza],
Using Expectation-Maximization for exposing image forgeries by revealing inconsistencies in shadow geometry,
JVCIR(58), 2019, pp. 323-333.
Elsevier DOI 1901
Image forensics, Forgery detection, Shadow geometry, EM algorithm, Image tampering BibRef

Zheng, L.[Lilei], Zhang, Y.[Ying], Thing, V.L.L.[Vrizlynn L.L.],
A survey on image tampering and its detection in real-world photos,
JVCIR(58), 2019, pp. 380-399.
Elsevier DOI 1901
Survey, Forgery Detection. Image tampering detection, Image forgery detection, Image forensics, Image copy-move detection, Image splicing detection BibRef

Fadl, S.M.[Sondos M.], Han, Q.[Qi], Li, Q.[Qiong],
Inter-frame forgery detection based on differential energy of residue,
IET-IPR(13), No. 3, February 2019, pp. 522-528.
DOI Link 1903
BibRef

Tu, B.[Bing], He, D.B.[Dan-Bing], Shang, Y.H.[Yong-Heng], Zhou, C.L.[Cheng-Le], Li, W.J.[Wu-Jing],
Deep feature representation for anti-fraud system,
JVCIR(59), 2019, pp. 253-256.
Elsevier DOI 1903
Convolutional neural network, Anti-fraud, Distance metric BibRef

George, S.[Sabu], Pai, M.M.M.[M.M. Manohara], Pai, R.M.[Radhika M.], Praharaj, S.K.[Samir Kumar],
Visual cues-based deception detection using two-class neural network,
IJCVR(9), No. 2, 2019, pp. 132-151.
DOI Link 1904
BibRef

Bappy, J.H., Simons, C., Nataraj, L., Manjunath, B.S., Roy-Chowdhury, A.K.,
Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries,
IP(28), No. 7, July 2019, pp. 3286-3300.
IEEE DOI 1906
data compression, decoding, image coding, image resolution, image segmentation, learning (artificial intelligence), decoder BibRef

Hosny, K.M.[Khalid M.], Hamza, H.M.[Hanaa M.], Lashin, N.A.[Nabil A.],
Copy-for-duplication forgery detection in colour images using QPCETMs and sub-image approach,
IET-IPR(13), No. 9, 18 July 2019, pp. 1437-1446.
DOI Link 1907
BibRef

Asghar, K.[Khurshid], Sun, X.[Xianfang], Rosin, P.L.[Paul L.], Saddique, M.[Mubbashar], Hussain, M.[Muhammad], Habib, Z.[Zulfiqar],
Edge-texture feature-based image forgery detection with cross-dataset evaluation,
MVA(30), No. 7-8, October 2019, pp. 1243-1262.
Springer DOI 1911
BibRef

Prakash, C.S.[Choudhary Shyam], Om, H.[Hari], Maheshkar, S.[Sushila],
Authentication of medical images using passive approach,
IET-IPR(13), No. 13, November 2019, pp. 2420-2427.
DOI Link 1911
Watermarks may interfere with the content of the medical image. polar cosine transform is used for feature extraction and the PatchMatch algorithm to locate the forged. BibRef

Wang, X.F.[Xiao-Feng], Zhou, X.R.[Xiao-Rui], Zhang, Q.[Qian], Xu, B.C.[Bing-Chao], Xue, J.R.[Jian-Ru],
Image alignment based perceptual image hash for content authentication,
SP:IC(80), 2020, pp. 115642.
Elsevier DOI 1912
Image alignment, Perceptual image hash, Geometric distortion-resilient, Image forging detection, Image tampering localization BibRef

Dua, S.[Shilpa], Singh, J.[Jyotsna], Parthasarathy, H.[Harish],
Detection and localization of forgery using statistics of DCT and Fourier components,
SP:IC(82), 2020, pp. 115778.
Elsevier DOI 2001
Discrete cosine transform, Doubly stochastic model, Image forgery detection, JPEG compression, Phase congruency BibRef

Kohli, A.[Aditi], Gupta, A.[Abhinav], Singhal, D.[Divya],
CNN based localisation of forged region in object-based forgery for HD videos,
IET-IPR(14), No. 5, 17 April 2020, pp. 947-958.
DOI Link 2004
BibRef

Nikoukhah, T.[Tina], Colom, M.[Miguel], Morel, J.M.[Jean-Michel], von Gioi, R.G.[Rafael Grompone],
Local JPEG Grid Detector via Blocking Artifacts, a Forgery Detection Tool,
IPOL(10), 2020, pp. 24-42.
DOI Link 2005
BibRef

Bammey, Q.[Quentin], von Gioi, R.G.[Rafael Grompone], Morel, J.M.[Jean-Michel],
Image Forgeries Detection through Mosaic Analysis: the Intermediate Values Algorithm,
IPOL(11), 2021, pp. 317-343.
DOI Link 2110
BibRef

Ahmed, B.[Belal], Gulliver, T.A.[T. Aaron], al Zahir, S.[Saif],
Image splicing detection using mask-RCNN,
SIViP(14), No. 5, July 2020, pp. 1035-1042.
Springer DOI 2006
BibRef

He, P., Li, H., Li, B., Wang, H., Liu, L.,
Exposing Fake Bitrate Videos Using Hybrid Deep-Learning Network From Recompression Error,
CirSysVideo(30), No. 11, November 2020, pp. 4034-4049.
IEEE DOI 2011
Videos, Bit rate, Feature extraction, Encoding, Standards, Quantization (signal), Forensics, Video forensics, hybrid deep-learning network BibRef

Fadl, S.[Sondos], Han, Q.[Qi], Li, Q.[Qiong],
CNN spatiotemporal features and fusion for surveillance video forgery detection,
SP:IC(90), 2021, pp. 116066.
Elsevier DOI 2012
Passive forensics, Convolution neural network, SSIM, Spatiotemporal features, Inter-frame forgeries BibRef

Selvaraj, P.[Priyadharsini], Karuppiah, M.[Muneeswaran],
Inter-frame forgery detection and localisation in videos using earth mover's distance metric,
IET-IPR(14), No. 16, 19 December 2020, pp. 4168-4177.
DOI Link 2103
BibRef

Singhal, D.[Divya], Gupta, A.[Abhinav],
Forgery localization in images based on joint statistics of image blocks with neighbouring blocks,
IET-IPR(15), No. 9, 2021, pp. 1893-1908.
DOI Link 2106
BibRef

Yu, M.M.[Miao-Miao], Zhang, J.[Jun], Li, S.[Shuohao], Lei, J.[Jun], Wang, F.L.[Feng-Lei], Zhou, H.[Hao],
Deep forgery discriminator via image degradation analysis,
IET-IPR(15), No. 11, 2021, pp. 2478-2493.
DOI Link 2108
BibRef

Zhao, L.[Lin], Chen, C.S.[Chang-Sheng], Huang, J.W.[Ji-Wu],
Deep Learning-Based Forgery Attack on Document Images,
IP(30), 2021, pp. 7964-7979.
IEEE DOI 2109
Forgery, Training, Authentication, Visualization, Task analysis, Security, Forensics, Document image, text editing, deep learning BibRef

Ananthi, M., Rajkumar, P., Sabitha, R., Karthik, S.,
A secure model on Advanced Fake Image-Feature Network (AFIFN) based on deep learning for image forgery detection,
PRL(152), 2021, pp. 260-266.
Elsevier DOI 2112
Image Forgery Detection, Real Image, Pair-wise Learning, Image Classification, AFIFN, Security BibRef

Cintas, C.[Celia], Speakman, S.[Skyler], Tadesse, G.A.[Girmaw Abebe], Akinwande, V.[Victor], McFowland, E.[Edward], Weldemariam, K.[Komminist],
Pattern detection in the activation space for identifying synthesized content,
PRL(153), 2022, pp. 207-213.
Elsevier DOI 2201
Subset scanning, Generative models, Synthetic content detection BibRef

Tan, S.Q.[Shun-Quan], Chen, B.Y.[Bao-Ying], Zeng, J.[Jishen], Li, B.[Bin], Huang, J.W.[Ji-Wu],
Hybrid deep-learning framework for object-based forgery detection in video,
SP:IC(105), 2022, pp. 116695.
Elsevier DOI 2205
Object-based video forgery, Hybrid deep learning, Video forensics, 3D convolution, Bi-directional LSTM BibRef

Yang, J.C.[Jia-Chen], Xiao, S.[Shuai], Li, A.[Aiyun], Lu, W.[Wen], Gao, X.B.[Xin-Bo], Li, Y.[Yang],
MSTA-Net: Forgery Detection by Generating Manipulation Trace Based on Multi-Scale Self-Texture Attention,
CirSysVideo(32), No. 7, July 2022, pp. 4854-4866.
IEEE DOI 2207
Faces, Forgery, Videos, Feature extraction, Databases, Information integrity, Generators, Trace generation, prob-tuple loss BibRef

Bammey, Q.[Quentin],
Analysis and Experimentation on the ManTraNet Image Forgery Detector,
IPOL(12), 2022, pp. 457-468.
DOI Link 2211

See also ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features. BibRef

Jaiswal, G.[Garima], Sharma, A.[Arun], Kumar Yadav, S.[Sumit],
DFD-SS: Document Forgery Detection using Spectral-Spatial Features for Hyperspectral Images,
JVCIR(89), 2022, pp. 103690.
Elsevier DOI 2212
Document forgery, Spectral, Spatial, Spectral-spatial, Autoencoders, Unsupervised Deep Learning BibRef

Yu, M.M.[Miao-Miao], Zhang, J.[Jun], Li, S.O.[Shu-Ohao], Lei, J.[Jun],
MSFRNet: Two-Stream Deep Forgery Detector via Multi-Scale Feature Extraction,
IET-IPR(17), No. 2, 2023, pp. 581-596.
DOI Link 2302
counterfactual causal reasoning, DeepFake detection, manipulation traces, multi-scale features BibRef

Chen, J.X.[Jia-Xin], Liao, X.[Xin], Wang, W.[Wei], Qian, Z.X.[Zhen-Xing], Qin, Z.[Zheng], Wang, Y.[Yaonan],
SNIS: A Signal Noise Separation-Based Network for Post-Processed Image Forgery Detection,
CirSysVideo(33), No. 2, February 2023, pp. 935-951.
IEEE DOI 2302
Forgery, Location awareness, Semantics, Feature extraction, Image coding, Blind source separation, Splicing, post-processed images BibRef

Bi, X.L.[Xiu-Li], Shang, Y.X.[Yi-Xuan], Liu, B.[Bo], Xiao, B.[Bin], Li, W.S.[Wei-Sheng], Gao, X.B.[Xin-Bo],
A Versatile Detection Method for Various Contrast Enhancement Manipulations,
CirSysVideo(33), No. 2, February 2023, pp. 491-504.
IEEE DOI 2302
Histograms, Forgery, Transform coding, Semantics, Authentication, Task analysis, Image coding, Global forgery detection, ZGS BibRef

Deng, X.[Xin], Zhao, B.[Bihe], Guan, Z.Y.[Zhen-Yu], Xu, M.[Mai],
New Finding and Unified Framework for Fake Image Detection,
SPLetters(30), 2023, pp. 90-94.
IEEE DOI 2302
Faces, Feature extraction, Forgery, Generative adversarial networks, Probability density function, non-local similarity BibRef

Chen, T.[Tong], Li, B.[Bin], Zeng, J.H.[Jin-Hua],
Learning Traces by Yourself: Blind Image Forgery Localization via Anomaly Detection With ViT-VAE,
SPLetters(30), 2023, pp. 150-154.
IEEE DOI 2303
Training, Location awareness, Forgery, Image reconstruction, Benchmark testing, Anomaly detection, Transformers, transformer BibRef

Fadl, S.[Sondos], Hosny, K.M.[Khalid M.], Hammad, M.[Mohamed],
Automatic fake document identification and localization using DE-Net and color-based features of foreign inks,
JVCIR(92), 2023, pp. 103801.
Elsevier DOI 2303
Handwriting forgery detection, Addition, Alteration, Document examination, Forged document, CNN BibRef

Shi, X.Q.[Xiao-Qian], Li, P.[Ping], Wu, H.[Hao], Chen, Q.D.[Qi-Dong], Zhu, H.Y.[Hao-Yu],
A lightweight image splicing tampering localization method based on MobileNetV2 and SRM,
IET-IPR(17), No. 6, 2023, pp. 1883-1892.
DOI Link 2305
dual-stream network, image tampering localization, lightweight convolutional network BibRef

Liu, Y.Q.[Ya-Qi], Lv, B.B.[Bin-Bin], Jin, X.[Xin], Chen, X.Y.[Xiao-Yu], Zhang, X.K.[Xiao-Kun],
TBFormer: Two-Branch Transformer for Image Forgery Localization,
SPLetters(30), 2023, pp. 623-627.
IEEE DOI 2306
Feature extraction, Transformers, Forgery, Location awareness, Fuses, Decoding, Convolution, Image forgery localization, two-branch, hierarchical-feature fusion BibRef

Li, D.[Dong], Zhu, J.Y.[Jia-Ying], Wang, M.[Menglu], Liu, J.W.[Jia-Wei], Fu, X.[Xueyang], Zha, Z.J.[Zheng-Jun],
Edge-aware Regional Message Passing Controller for Image Forgery Localization,
CVPR23(8222-8232)
IEEE DOI 2309
BibRef

Guillaro, F.[Fabrizio], Cozzolino, D.[Davide], Sud, A.[Avneesh], Dufour, N.[Nicholas], Verdoliva, L.[Luisa],
TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization,
CVPR23(20606-20615)
IEEE DOI 2309
BibRef

Shi, Z.[Zenan], Shen, X.[Xuanjing], Chen, H.P.[Hai-Peng], Lyu, Y.[Yingda],
PL-GNet: Pixel Level Global Network for detection and localization of image forgeries,
SP:IC(119), 2023, pp. 117029.
Elsevier DOI 2310
Image forgery detection and localization, Global network, Atrous convolution, Decoding net, Encoding net BibRef

Mehrjardi, F.Z.[Fatemeh Zare], Latif, A.M.[Ali Mohammad], Zarchi, M.S.[Mohsen Sardari], Sheikhpour, R.[Razieh],
A survey on deep learning-based image forgery detection,
PR(144), 2023, pp. 109778.
Elsevier DOI 2310
Forgery detection, Deep learning, Inpainting, Copy move, Splicing, Tampered image, CNN, RNN, R-CNN, Auto-Encoder BibRef

He, D.[Defen], Jiang, Q.[Qian], Jin, X.[Xin], Cheng, Z.[Zien], Liu, S.[Shuai], Yao, S.[Shaowen], Zhou, W.[Wei],
MCDC-Net: Multi-scale forgery image detection network based on central difference convolution,
IET-IPR(18), No. 1, 2024, pp. 1-12.
DOI Link 2401
computer vision, convolutional neural nets, convolution, feature extraction, image processing, image recognition, supervised learning BibRef

Luo, Y.J.[Yuan-Jing], Zhou, T.Q.[Tong-Qing], Cui, S.L.[Sheng-Lan], Ye, Y.F.[Yun-Fan], Liu, F.[Fang], Cai, Z.P.[Zhi-Ping],
Fixing the Double Agent Vulnerability of Deep Watermarking: A Patch-Level Solution Against Artwork Plagiarism,
CirSysVideo(34), No. 3, March 2024, pp. 1670-1683.
IEEE DOI 2403
Watermarking, Plagiarism, Training, Robustness, Decoding, Distortion, Copyright protection, Deep watermarking, convolutional neural networks BibRef

Melman, A.[Anna], Evsutin, O.[Oleg],
Methods for countering attacks on image watermarking schemes: Overview,
JVCIR(99), 2024, pp. 104073.
Elsevier DOI 2403
Digital images, Watermarking, Robustness, Removal attacks, Forgery attacks BibRef

Boato, G.[Giulia], de Natale, F.G.B.[Francesco G.B.], de Stefano, G.[Gianluca], Pasquini, C.[Cecilia], Roli, F.[Fabio],
Adversarial mimicry attacks against image splicing forensics: An approach for jointly hiding manipulations and creating false detections,
PRL(179), 2024, pp. 73-79.
Elsevier DOI 2403
Adversarial multimedia forensics, Gray-box attack, Image manipulation hiding, False forgery creation, Image splicing detection BibRef


Balan, K.[Kar], Agarwal, S.[Shruti], Jenni, S.[Simon], Parsons, A.[Andy], Gilbert, A.[Andrew], Collomosse, J.[John],
EKILA: Synthetic Media Provenance and Attribution for Generative Art,
WMF23(913-922)
IEEE DOI 2309
BibRef

Corvi, R.[Riccardo], Cozzolino, D.[Davide], Poggi, G.[Giovanni], Nagano, K.[Koki], Verdoliva, L.[Luisa],
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models,
WMF23(973-982)
IEEE DOI 2309
BibRef

Guo, X.[Xiao], Liu, X.H.[Xiao-Hong], Ren, Z.Y.[Zhi-Yuan], Grosz, S.[Steven], Masi, I.[Iacopo], Liu, X.M.[Xiao-Ming],
Hierarchical Fine-Grained Image Forgery Detection and Localization,
CVPR23(3155-3165)
IEEE DOI 2309
BibRef

Niloy, F.F.[Fahim Faisal], Bhaumik, K.K.[Kishor Kumar], Woo, S.S.[Simon S.],
CFL-Net: Image Forgery Localization Using Contrastive Learning,
WACV23(4631-4640)
IEEE DOI 2302
Location awareness, Measurement, Fuses, Image edge detection, Transform coding, Focusing, Benchmark testing, Social good BibRef

Ma, J.W.[Jing-Wei], Chai, L.[Lucy], Huh, M.Y.[Min-Young], Wang, T.Z.[Tong-Zhou], Lim, S.N.[Ser-Nam], Isola, P.[Phillip], Torralba, A.[Antonio],
Totems: Physical Objects for Verifying Visual Integrity,
ECCV22(XIV:164-180).
Springer DOI 2211
BibRef

Liu, B.[Bo], Yang, F.[Fan], Bi, X.L.[Xiu-Li], Xiao, B.[Bin], Li, W.S.[Wei-Sheng], Gao, X.B.[Xin-Bo],
Detecting Generated Images by Real Images,
ECCV22(XIV:95-110).
Springer DOI 2211
BibRef

Gudavalli, C.[Chandrakanth], Rosten, E.[Erik], Nataraj, L.[Lakshmanan], Chandrasekaran, S.[Shivkumar], Manjunath, B.S.,
SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery,
WMF22(1-11)
IEEE DOI 2210
Location awareness, Measurement, Satellites, Image coding, Roads BibRef

Wu, H.W.[Hai-Wei], Zhou, J.T.[Jian-Tao], Tian, J.[Jinyu], Liu, J.[Jun],
Robust Image Forgery Detection over Online Social Network Shared Images,
CVPR22(13430-13439)
IEEE DOI 2210
Training, Social networking (online), Detectors, Predictive models, Propagation losses, Forgery, Transparency, fairness, accountability, Vision applications and systems BibRef

Das, S.[Sowmen], Islam, M.S.[Md. Saiful], Amin, M.R.[Md. Ruhul],
GCA-Net: Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection,
WMF22(81-90)
IEEE DOI 2210
Location awareness, Training, Semantics, Logic gates, Benchmark testing, Forgery, Robustness BibRef

Rao, Y.[Yuan], Ni, J.Q.[Jiang-Qun],
Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images,
ICCV21(15014-15023)
IEEE DOI 2203
Location awareness, Image coding, Social networking (online), Splicing, Transform coding, Computer architecture, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Hao, J.[Jing], Zhang, Z.X.[Zhi-Xin], Yang, S.[Shicai], Xie, D.[Di], Pu, S.L.[Shi-Liang],
TransForensics: Image Forgery Localization with Dense Self-Attention,
ICCV21(15035-15044)
IEEE DOI 2203
Location awareness, Learning systems, Image forensics, Semantics, Object detection, Transformers, Solids, grouping and shape BibRef

Bammey, Q.[Quentin], von Gioi, R.G.[Rafael Grompone], Morel, J.M.[Jean-Michel],
Forgery Detection by Internal Positional Learning of Demosaicing Traces,
WACV22(1019-1029)
IEEE DOI 2202
Training, Image coding, Image color analysis, Forensics, Neural networks, Transform coding, Transfer, Few-shot, Semi- and Un- supervised Learning Image forensics BibRef

Saire, D.[Darwin], Tabbone, S.A.[Salvatore A.],
Documents Counterfeit Detection Through a Deep Learning Approach,
ICPR21(3915-3922)
IEEE DOI 2105
Deep learning, Visualization, Image resolution, Neural networks, Predictive models, Benchmark testing, Feature extraction BibRef

Jeong, Y.H.[Yong-Hyun], Choi, J.W.[Jong-Won], Kim, D.[Doyeon], Park, S.[Sehyeon], Hong, M.[Minki], Park, C.H.[Chang-Hyun], Min, S.[Seungjai], Gwon, Y.[Youngjune],
Dofnet: Depth of Field Difference Learning for Detecting Image Forgery,
ACCV20(VI:83-100).
Springer DOI 2103
BibRef

Bai, Y., Guo, Y., Wei, J., Lu, L., Wang, R., Wang, Y.,
Fake Generated Painting Detection Via Frequency Analysis,
ICIP20(1256-1260)
IEEE DOI 2011
Painting, Feature extraction, Frequency-domain analysis, Databases, Testing, Forgery, Support vector machines, Image Forgery Detection, Fourier Transform BibRef

Qian, Y.Y.[Yu-Yang], Yin, G.J.[Guo-Jun], Sheng, L.[Lu], Chen, Z.X.[Zi-Xuan], Shao, J.[Jing],
Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues,
ECCV20(XII: 86-103).
Springer DOI 2010
BibRef

Wang, T.[Tao], Du, M.[Maggie], Wu, X.M.[Xin-Min], He, T.P.[Tai-Ping],
An Analytical Framework for Trusted Machine Learning and Computer Vision Running with Blockchain,
TCV20(32-38)
IEEE DOI 2008
How to trust the results. Machine learning, Computational modeling, Servers, Contracts, Automation BibRef

Kumar, A., Bhavsar, A., Verma, R.,
Syn2Real: Forgery Classification via Unsupervised Domain Adaptation,
WACVWS20(63-70)
IEEE DOI 2006
Forgery, Feature extraction, Semantics, Image edge detection, Task analysis, Adaptation models, Splicing BibRef

Li, H., Huang, J.,
Localization of Deep Inpainting Using High-Pass Fully Convolutional Network,
ICCV19(8300-8309)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, image filtering, image recognition, Probability BibRef

Wu, Y.[Yue], AbdAlmageed, W.[Wael], Natarajan, P.[Premkumar],
ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features,
CVPR19(9535-9544).
IEEE DOI 2002
Code:
See also Analysis and Experimentation on the ManTraNet Image Forgery Detector. BibRef

McCloskey, S., Albright, M.,
Detecting GAN-Generated Imagery Using Saturation Cues,
ICIP19(4584-4588)
IEEE DOI 1910
BibRef

Jenni, S.[Simon], Favaro, P.[Paolo],
Self-Supervised Feature Learning by Learning to Spot Artifacts,
CVPR18(2733-2742)
IEEE DOI 1812
Real images vs. synthetic. Maintenance engineering, Decoding, Task analysis, Training, Feature extraction, Image segmentation BibRef

Roy, A., Tariang, D.B., Chakraborty, R.S., Naskar, R.,
Discrete Cosine Transform Residual Feature Based Filtering Forgery and Splicing Detection in JPEG Images,
PRIV18(1633-16338)
IEEE DOI 1812
Discrete cosine transforms, Feature extraction, Forgery, Splicing, Transform coding, Histograms, Forensics BibRef

Privman-Horesh, N., Haider, A., Hel-Or, H.,
Forgery Detection in 3D-Sensor Images,
PRIV18(1642-16428)
IEEE DOI 1812
Cameras, Forgery, Sensors, Lighting BibRef

Artaud, C., Sidčre, N., Doucet, A., Ogier, J., Yooz, V.P.D.,
Find it! Fraud Detection Contest Report,
ICPR18(13-18)
IEEE DOI 1812
Task analysis, Forgery, Tools, XML, Companies, Optical character recognition software, Training BibRef

Zhang, Z., Zhang, Y., Zhou, Z., Luo, J.,
Boundary-based Image Forgery Detection by Fast Shallow CNN,
ICPR18(2658-2663)
IEEE DOI 1812
Forgery, Image edge detection, Image resolution, Feature extraction, Image coding, Signal processing BibRef

Kuznetsov, A.[Andrey],
Camera Sensor Traces Analysis in Image Forgery Detection Problem,
ICCVG18(453-463).
Springer DOI 1810
BibRef

Maigrot, C., Kijak, E., Claveau, V.,
Context-Aware Forgery Localization in Social-Media Images: A Feature-Based Approach Evaluation,
ICIP18(545-549)
IEEE DOI 1809
Social network services, Splicing, Forgery, Kernel, Estimation, Image forensics, Measurement, Forgery localization, Image tampering BibRef

Long, C., Smith, E., Basharat, A., Hoogs, A.,
A C3D-Based Convolutional Neural Network for Frame Dropping Detection in a Single Video Shot,
MedForen17(1898-1906)
IEEE DOI 1709
Cameras, Color, Feature extraction, Forgery, Histograms, Support vector machines, Training BibRef

Bunk, J., Bappy, J.H., Mohammed, T.M., Nataraj, L., Flenner, A., Manjunath, B.S., Chandrasekaran, S., Roy-Chowdhury, A.K., Peterson, L.,
Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning,
MedForen17(1881-1889)
IEEE DOI 1709
Feature extraction, Heating systems, Image segmentation, Machine learning, Neural networks, Radon, Transform, coding BibRef

Zhou, J.H.[Jiang-Hong], Ni, J.Q.[Jiang-Qun], Rao, Y.[Yuan],
Block-Based Convolutional Neural Network for Image Forgery Detection,
IWDW17(65-76).
Springer DOI 1708
BibRef

Vieira, R.[Rafael], Antunes, M.[Mário], Silva, C.[Catarina], Assis, A.[Ana],
Automatic Documents Counterfeit Classification Using Image Processing and Analysis,
IbPRIA17(400-407).
Springer DOI 1706
BibRef

Cattaneo, G.[Giuseppe], Roscigno, G.[Gianluca], Bruno, A.[Andrea],
Using PNU-Based Techniques to Detect Alien Frames in Videos,
ACIVS16(735-746).
Springer DOI 1611
Apply techniques like in camera id BibRef

Luo, Z.[Zhipei], Shafait, F.[Faisal], Mian, A.[Ajmal],
Localized forgery detection in hyperspectral document images,
ICDAR15(496-500)
IEEE DOI 1511
BibRef

Cattaneo, G.[Giuseppe], Ferraro Petrillo, U., Roscigno, G.[Gianluca], de Fusco, C.,
A PNU-based technique to detect forged regions in digital images,
ACIVS15(486-498)
Springer DOI 1611
BibRef

Julliand, T.[Thibault], Nozick, V.[Vincent], Talbot, H.[Hugues],
Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images,
ACIVS16(126-134).
Springer DOI 1611
BibRef

Buchana, P., Cazan, I., Diaz-Granados, M., Juefei-Xu, F., Savvides, M.,
Simultaneous forgery identification and localization in paintings using advanced correlation filters,
ICIP16(146-150)
IEEE DOI 1610
Art BibRef

Mathai, M., Rajan, D., Emmanuel, S.,
Video forgery detection and localization using normalized cross-correlation of moment features,
Southwest16(149-152)
IEEE DOI 1605
Computers BibRef

Zheng, L.[Lu], Sun, T.[Tanfeng], Shi, Y.Q.[Yun-Qing],
Inter-frame Video Forgery Detection Based on Block-Wise Brightness Variance Descriptor,
IWDW14(18-30).
Springer DOI 1602
BibRef

Wang, W.[Wan], Jiang, X.H.[Xing-Hao], Wang, S.L.[Shi-Lin], Wan, M.[Meng], Sun, T.F.[Tan-Feng],
Identifying Video Forgery Process Using Optical Flow,
IWDW13(244-257).
Springer DOI 1407
BibRef

Lin, X.F.[Xu-Feng], Li, C.T.[Chang-Tsun], Hu, Y.J.[Yong-Jian],
Exposing image forgery through the detection of contrast enhancement,
ICIP13(4467-4471)
IEEE DOI 1402
Digital forensics BibRef

Saleh, S.Q.[Sahar Q.], Hussain, M.[Muhammad], Muhammad, G.[Ghulam], Bebis, G.N.[George N.],
Evaluation of Image Forgery Detection Using Multi-scale Weber Local Descriptors,
ISVC13(II:416-424).
Springer DOI 1311
BibRef

Cozzolino, D.[Davide], Gargiulo, F.[Francesco],
Multiple Classifier Systems for Image Forgery Detection,
CIAP13(II:259-268).
Springer DOI 1309
BibRef

Cozzolino, D.[Davide], Poggi, G.[Giovanni], Sansone, C.[Carlo], Verdoliva, L.[Luisa],
A Comparative Analysis of Forgery Detection Algorithms,
SSSPR12(693-700).
Springer DOI 1211
BibRef

Zach, F.[Fabian], Riess, C.[Christian], Angelopoulou, E.[Elli],
Automated Image Forgery Detection through Classification of Jpeg Ghosts,
DAGM12(185-194).
Springer DOI 1209
BibRef

Al-Hammadi, M.H.[Muneer H.], Muhammad, G.[Ghulam], Hussain, M.[Muhammad], Bebis, G.N.[George N.],
Curvelet Transform and Local Texture Based Image Forgery Detection,
ISVC13(II:503-512).
Springer DOI 1311
BibRef

Polatkan, G.[Gungor], Jafarpour, S.[Sina], Brasoveanu, A.[Andrei], Hughes, S.[Shannon], Daubechies, I.[Ingrid],
Detection of forgery in paintings using supervised learning,
ICIP09(2921-2924).
IEEE DOI 0911
Not through watermarks, but forgery detection. BibRef

Zheng, J.B.[Jiang-Bin], Liu, M.[Miao],
A Digital Forgery Image Detection Algorithm Based on Wavelet Homomorphic Filtering,
DW08(152-160).
Springer DOI 0811
BibRef

Li, Z.[Zhe], Zheng, J.B.[Jiang-Bin],
Blind Detection of Digital Forgery Image Based on the Local Entropy of the Gradient,
DW08(161-169).
Springer DOI 0811
BibRef

Luo, W.Q.[Wei-Qi], Huang, J.W.[Ji-Wu], Qiu, G.P.[Guo-Ping],
A Novel Method for Block Size Forensics Based on Morphological Operations,
DW08(229-239).
Springer DOI 0811
BibRef
Earlier:
Robust Detection of Region-Duplication Forgery in Digital Image,
ICPR06(IV: 746-749).
IEEE DOI 0609
BibRef

Guo, J.K.[Jinhong Katherine],
Forgery Detection by Local Correspondence,
UMD--TR4122, April 2000.
WWW Link. BibRef 0004

Guo, J.K., Doermann, D.S., Rosenfeld, A.,
Off-line Skilled Forgery Detection Using Stroke and Substroke Properties,
ICPR00(Vol II: 355-358).
IEEE DOI 0009
BibRef
Earlier:
Local Correspondence for Detecting Random Forgeries,
ICDAR97(319-323).
IEEE DOI 9708
BibRef

Chapter on OCR, Document Analysis and Character Recognition Systems continues in
Tamper Detection, Image Manipulation Detection, Forensics .


Last update:Apr 10, 2024 at 09:54:40