Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
Abstract
Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
2013 IEEE International Conference on Computer Vision
(2013) · doi:10.1109/iccv19984.2013 · Cited by 4,232 · see citing works
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144 · see citing works
NetVLAD: CNN architecture for weakly supervised place recognitionPreprint
Arandjelović, R., Gronát, P., Torii, A., Pajdla, T., & Šivic, J. (2015) · arXiv (Cornell University) · Cited by 1,790 · see citing works
Gray, R. M., & Neuhoff, D. L. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.720541 · Cited by 1,647 · see citing works
Steganalysis by Subtractive Pixel Adjacency Matrix
Pevný, T., Bas, P., & Fridrich, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2045842 · Cited by 899 · see citing works
A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer
Bayar, B., & Stamm, M. C. (2016) · Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/2909827.2930786 · Cited by 838 · see citing works
Steganalysis by subtractive pixel adjacency matrix
Pevný, T., Bas, P., & Fridrich, J. (2009) · ACM workshop on Multimedia and security · doi:10.1145/1597817.1597831 · Cited by 771 · see citing works
Exposing digital forgeries by detecting traces of resampling
Popescu, A., & Farid, H. (2005) · doi:10.1109/tsp.2004.839932(410 · Cited by 667 · see citing works
Deep learning for steganalysis via convolutional neural networks
Qian, Y., Dong, J., Wang, W., & Tan, T. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083479 · Cited by 556 · see citing works
2007 IEEE International Conference on Image Processing
SABRI, A., Boonært, J., Lecoeuche, S., & MOUADDIB, E. M. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379496 · Cited by 547 · see citing works
Forensic detection of image manipulation using statistical intrinsic fingerprints
Stamm, M. C., & Liu, K. J. R. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2053202 · Cited by 323 · see citing works
Splicebuster: A new blind image splicing detector
Cozzolino, D., Poggi, G., & Verdoliva, L. (2015) · 2015 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2015.7368565 · Cited by 283 · see citing works
Self-citationOn detection of median filtering in digital images
Kirchner, M., & Fridrich, J. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.839100 · Cited by 279 · see citing works
Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue
Kirchner, M. (2008) · ACM workshop on Multimedia and security · doi:10.1145/1411328.1411333 · Cited by 236 · see citing works
Detecting Double JPEG Compression With the Same Quantization Matrix
Huang, F., Huang, J., & Shi, Y. Q. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2072921 · Cited by 203 · see citing works
Identification of Various Image Operations Using Residual-Based Features
Li, H., Luo, W., Qiu, X., & Huang, J. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2599849 · Cited by 165 · see citing works
Single-image splicing localization through autoencoder-based anomaly detection
Cozzolino, D., & Verdoliva, L. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823921 · Cited by 122 · see citing works
Self-citationCozzolino, D., Gragnaniello, D., & Verdoliva, L. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026073 · Cited by 112 · see citing works
Self-citationImage forgery detection through residual-based local descriptors and block-matching
Cozzolino, D., Gragnaniello, D., & Verdoliva, L. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026072 · Cited by 112 · see citing works
Self-citationA feature-based approach for image tampering detection and localization
Verdoliva, L., Cozzolino, D., & Poggi, G. (2014) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2014.7084319 · Cited by 78 · see citing works
Self-citationGeneral-purpose image forensics using patch likelihood under image statistical models
Fan, W., Wang, K., & Cayre, F. (2015) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2015.7368606 · Cited by 66 · see citing works
A bag-of-words equivalent recurrent neural network for action recognition
Richard, A., & Gall, J. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.014 · Cited by 41 · see citing works
Manipulation Detection on Image Patches Using FusionBoost
Cao, H., & Kot, A. C. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2185696 · Cited by 40 · see citing works
Scalable Processing History Detector for JPEG Images
Boroumand, M., & Fridrich, J. (2017) · Electronic Imaging · doi:10.2352/issn.2470-1173.2017.7.mwsf-336 · Cited by 20 · see citing works
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FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314 · see citing works
Self-citationDeepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237 · see citing works
Face X-Ray for More General Face Forgery Detection
Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211 · see citing works
Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058 · see citing works
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Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019 · see citing works
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979 · see citing works
Self-citationCapsule-forensics: Using Capsule Networks to Detect Forged Images and Videos
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682602 · Cited by 951 · see citing works
Learning Rich Features for Image Manipulation Detection
Zhou, P., Han, X., Morariu, V. I., & Davis, L. S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00116 · Cited by 837 · see citing works
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00083 · Cited by 600 · see citing works
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Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00296 · Cited by 599 · see citing works
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Nguyen, H. H., Fang, F., Yamagishi, J., & Echizen, I. (2019) · 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) · doi:10.1109/btas46853.2019.9185974 · Cited by 549 · see citing works
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Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00084 · Cited by 399 · see citing works
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Salloum, R., Ren, Y., & Kuo, C. ‐. J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.01.010 · Cited by 401 · see citing works
DeepFake Detection by Analyzing Convolutional Traces
Guarnera, L., Giudice, O., & Battiato, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00341 · Cited by 374 · see citing works
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Wang, J., Wu, Z., Ouyang, W., Han, X., Chen, J., Jiang, Y.-G., & Li, S.-N. (2022) · Proceedings of the 2022 International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531415 · Cited by 368 · see citing works
Li, J., Xie, H., Li, J., Wang, Z., & Zhang, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00639 · Cited by 357 · see citing works
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Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., & Ji, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16193 · Cited by 334 · see citing works
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Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects
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Li, X., Lang, Y., Chen, Y., Mao, X., He, Y., Wang, S., Xue, H., & Lu, Q. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414034 · Cited by 192 · see citing works
MTD-Net: Learning to Detect Deepfakes Images by Multi-Scale Texture Difference
Yang, J., Li, A., Xiao, S., Lu, W., & Gao, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3102487 · Cited by 184 · see citing works
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Haliassos, A., Mira, R., Petridis, S., & Pantic, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01453 · Cited by 180 · see citing works
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Tariq, S., Lee, S., Kim, H., Shin, Y., & Woo, S. S. (2018) · doi:10.1145/3267357.3267367 · Cited by 177 · see citing works
A survey on image tampering and its detection in real-world photos
Zheng, L., Zhang, Y., & Thing, V. L. L. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.12.022 · Cited by 175 · see citing works
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Fake face detection via adaptive manipulation traces extraction network
Guo, Z., Yang, G., Chen, J., & Sun, X. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103170 · Cited by 165 · see citing works
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Yang, Z., Liang, J., Xu, Y., Zhang, X., & He, R. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3249566 · Cited by 151 · see citing works
CORE: Consistent Representation Learning for Face Forgery Detection
Ni, Y., Meng, D., Yu, C., Quan, C., Ren, D., & Zhao, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00011 · Cited by 136 · see citing works
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2020) · IEEE Access · doi:10.1109/access.2020.3009877 · Cited by 140 · see citing works
Self-citationA Comprehensive Review of Deep-Learning-Based Methods for Image Forensics
Camacho, I. C., & Wang, K. (2021) · Journal of Imaging · doi:10.3390/jimaging7040069 · Cited by 133 · see citing works
Use of a Capsule Network to Detect Fake Images and VideosPreprint
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12467 · Cited by 163 · see citing works
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Thakur, R., & Rohilla, R. (2020) · Forensic Science International · doi:10.1016/j.forsciint.2020.110311 · Cited by 129 · see citing works
Detect and locate: Exposing face manipulation by semantic-and noise-level telltales
Kong, C., Chen, B., Li, H., Wang, S., Rocha, A., & Kwong, S. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3169921 · Cited by 120 · see citing works
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Du, M., Pentyala, S., Li, Y., & Hu, X. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3411892 · Cited by 114 · see citing works
Deep Matching and Validation Network
Wu, Y., AbdAlmageed, W., & Natarajan, P. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123411 · Cited by 114 · see citing works
MARLIN: Masked Autoencoder for facial video Representation LearnINg
Cai, Z., Ghosh, S., Stefanov, K., Dhall, A., Cai, J., Rezatofighi, H., Haffari, R., & Hayat, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00150 · Cited by 113 · see citing works
Hierarchical Frequency-Assisted Interactive Networks for Face Manipulation Detection
Miao, C.-T., Tan, Z., Chu, Q., Yu, N.-H., & Guo, G.-D. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3198275 · Cited by 110 · see citing works
Deep Learning Local Descriptor for Image Splicing Detection and Localization
Rao, Y., Ni, J., & Zhao, H. (2020) · IEEE Access · doi:10.1109/access.2020.2970735 · Cited by 107 · see citing works
PRRNet: Pixel-Region relation network for face forgery detection
Shang, Z., Xie, H., Zha, Z.-J., Yu, L., Li, Y., & Zhang, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107950 · Cited by 107 · see citing works
Image Forgery Localization based on Multi-Scale Convolutional Neural Networks
Liu, Y., Guan, Q., Zhao, X., & Cao, Y. (2018) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3206004.3206010 · Cited by 106 · see citing works
CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis
Barni, M., Kallas, K., Nowroozi, E., & Tondi, B. (2020) · 2020 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs49906.2020.9360905 · Cited by 103 · see citing works
Two-Stage Copy-Move Forgery Detection With Self Deep Matching and Proposal SuperGlue
Liu, Y., Xia, C. Y., Zhu, X., & Xu, S. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3132828 · Cited by 102 · see citing works
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Jia, S., Ma, C., Yao, T., Yin, B., Ding, S., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00407 · Cited by 97 · see citing works
SSTNet: Detecting Manipulated Faces Through Spatial, Steganalysis and Temporal Features
Wu, X., Xie, Z., Gao, Y., & Xiao, Y. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9053969 · Cited by 94 · see citing works
Ganguly, S., Ganguly, A., Mohiuddin, S., Malakar, S., & Sarkar, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118423 · Cited by 101 · see citing works
Lee, S., Tariq, S., Shin, Y., & Woo, S. S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107256 · Cited by 93 · see citing works
One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework
Tariq, S., Lee, S., & Woo, S. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449809 · Cited by 91 · see citing works
Liu, Y., Zhu, X., Zhao, X., & Cao, Y. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2902826 · Cited by 92 · see citing works
A Survey of Deep Learning-Based Source Image Forensics
Yang, P., Baracchi, D., Ni, R., Zhao, Y., Argenti, F., & Piva, A. (2020) · Journal of Imaging · doi:10.3390/jimaging6030009 · Cited by 116 · see citing works
Detecting Deepfakes with Metric Learning
Kumar, A., Bhavsar, A., & Verma, R. (2020) · 2020 8th International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf49977.2020.9107962 · Cited by 90 · see citing works
Sharma, P., Kumar, M., & Sharma, H. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13808-w · Cited by 90 · see citing works
Detecting fake images by identifying potential texture difference
Yang, J., Xiao, S., Li, A., Lan, G., & Wang, H. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.043 · Cited by 86 · see citing works
Detecting DeepFake, FaceSwap and Face2Face facial forgeries using frequency CNN
Kohli, A., & Gupta, A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10420-8 · Cited by 90 · see citing works
Digital image forgery detection: a systematic scrutiny
Walia, S., & Kumar, K. (2018) · Australian Journal of Forensic Sciences · doi:10.1080/00450618.2018.1424241 · Cited by 83 · see citing works
Video Transformer for Deepfake Detection with Incremental Learning
Khan, S. A., & Dai, H. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475332 · Cited by 79 · see citing works
Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM
Han, B., Han, X., Zhang, H., Li, J., & Cao, X. (2021) · IEEE Transactions on Biometrics, Behavior, and Identity Science · doi:10.1109/tbiom.2021.3065735 · Cited by 77 · see citing works
Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks
Kim, D., Jang, H., Mun, S.-M., Choi, S., & Lee, H.-K. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2782363 · Cited by 77 · see citing works
LiSiam: Localization Invariance Siamese Network for Deepfake Detection
Wang, J., Sun, Y., & Tang, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3186803 · Cited by 73 · see citing works
Detection of Deepfake Videos Using Long-Distance Attention
Lu, W., Liu, L., Zhang, B., Luo, J., Zhao, X., Zhou, Y., & Huang, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3233063 · Cited by 75 · see citing works
Wavelet-packets for deepfake image analysis and detection
Wolter, M., Blanke, F., Heese, R., & Garcke, J. (2022) · Machine Learning · doi:10.1007/s10994-022-06225-5 · Cited by 74 · see citing works
Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries
Samir, S., Emary, E., El-Sayed, K., & Onsi, H. M. (2020) · Information · doi:10.3390/info11050275 · Cited by 67 · see citing works
Tariq, S., Lee, S., Kim, H., Shin, Y., & Woo, S. S. (2019) · ACM Symposium on Applied Computing · doi:10.1145/3297280.3297410 · Cited by 66 · see citing works
Exploiting Prediction Error Inconsistencies through LSTM-based Classifiers to Detect Deepfake Videos
Amerini, I., & Caldelli, R. (2020) · Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/3369412.3395070 · Cited by 64 · see citing works
Explaining Deepfake Detection by Analysing Image Matching
Dong, S., Wang, J., Liang, J., Fan, H., & Ji, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19781-9_2 · Cited by 65 · see citing works
Inconsistency-Aware Wavelet Dual-Branch Network for Face Forgery Detection
Jia, G., Zheng, M., Hu, C., Ma, X., Xu, Y., Liu, L., Deng, Y., & He, R. (2021) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3086109 · Cited by 66 · see citing works
A Convolutional LSTM based Residual Network for Deepfake Video DetectionPreprint
Tariq, S., Lee, S., & Woo, S. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.07480 · Cited by 60 · see citing works
A video is worth more than 1000 lies. Comparing 3DCNN approaches for detecting deepfakes
Wang, Y., & Dantcheva, A. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00089 · Cited by 60 · see citing works
Exposing Fake Faces Through Deep Neural Networks Combining Content and Trace Feature Extractors
Kim, E., & Cho, S. (2021) · IEEE Access · doi:10.1109/access.2021.3110859 · Cited by 59 · see citing works
GLFF: Global and Local Feature Fusion for AI-Synthesized Image Detection
Ju, Y., Jia, S., Cai, J., Guan, H., & Lyu, S. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3313503 · Cited by 60 · see citing works
A lightweight 3D convolutional neural network for deepfake detection
Liu, J., Zhu, K., Lu, W., Luo, X., & Zhao, X. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22499 · Cited by 57 · see citing works
Deepfake Video Detection Based on MesoNet with Preprocessing Module
Xia, Z., Qiao, T., Xu, M., Wu, X., Han, L., & Chen, Y. (2022) · Symmetry · doi:10.3390/sym14050939 · Cited by 56 · see citing works
Exploring Disentangled Content Information for Face Forgery Detection
Liang, J., Shi, H., & Deng, W. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19781-9_8 · Cited by 58 · see citing works
Preliminary Forensics Analysis of DeepFake Images
Guarnera, L., Giudice, O., Nastasi, C., & Battiato, S. (2020) · AEIT International Annual Conference · doi:10.23919/aeit50178.2020.9241108 · Cited by 57 · see citing works
Depth map guided triplet network for deepfake face detection
Liang, B., Wang, Z., Huang, B., Zou, Q., Wang, Q., & Liang, J. (2023) · Neural Networks · doi:10.1016/j.neunet.2022.11.031 · Cited by 54 · see citing works
Nguyen, X. H., Tran, T. S., Le, V. T., Nguyen, K. D., & Truong, D.-T. (2021) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2021.301108 · Cited by 53 · see citing works
Social Network Identification Through Image Classification With CNN
Amerini, I., Li, C., & Caldelli, R. (2019) · IEEE Access · doi:10.1109/access.2019.2903876 · Cited by 53 · see citing works
Deepfake Forensics via an Adversarial Game
Zhi, W., Guo, Y., & Zuo, W. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3172845 · Cited by 51 · see citing works
TAR: Generalized Forensic Framework to Detect Deepfakes Using Weakly Supervised Learning
Lee, S., Tariq, S., Kim, J., & Woo, S. S. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-78120-0_23 · Cited by 54 · see citing works
Dynamic texture analysis for detecting fake faces in video sequences
Bonomi, M., Pasquini, C., & Boato, G. (2021) · Institutional Research Information System (Università degli Studi di Trento) · doi:10.1016/j.jvcir.2021.103239 · Cited by 51 · see citing works
Image Manipulation Localization Using Attentional Cross-Domain CNN Features
Li, S., Xu, S., Ma, W., & Zong, Q. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3130168 · Cited by 51 · see citing works
An Information Theoretic Approach for Attention-Driven Face Forgery Detection
Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., & Ji, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19781-9_7 · Cited by 51 · see citing works
Learning Forgery Region-Aware and ID-Independent Features for Face Manipulation Detection
Miao, C.-T., Chu, Q., Li, W.-H., Li, S., Tan, Z., Zhuang, W., & Yu, N.-H. (2022) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3119403 · Cited by 48 · see citing works
Image forgery detection: comprehensive review of digital forensics approaches
Singh, S. P., & Kumar, R. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00265-8 · Cited by 56 · see citing works
Multi-attention-based approach for deepfake face and expression swap detection and localization
Waseem, S., Abu–Bakar, S. A. R., Omar, Z., Ahmed, B. A., Baloch, S., & Hafeezallah, A. (2023) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-023-00614-z · Cited by 52 · see citing works
A review of digital video tampering: From simple editing to full synthesis
Johnston, P., & Elyan, E. (2019) · Digital Investigation · doi:10.1016/j.diin.2019.03.006 · Cited by 51 · see citing works
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
Larue, N., Vu, N., Štruc, V., Peer, P., & Christophides, V. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01921 · Cited by 51 · see citing works
Wang, G., Jiang, Q., Jin, X., Li, W., & Cui, X. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109114 · Cited by 49 · see citing works
Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey
Pham, N. T., & Park, C. (2023) · IEEE Access · doi:10.1109/access.2023.3241837 · Cited by 46 · see citing works
Learning Pairwise Interaction for Generalizable DeepFake Detection
Xu, Y., Raja, K. B., Verdoliva, L., & Pedersen, M. (2023) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw58289.2023.00074 · Cited by 46 · see citing works
Self-citationVisual attention-based deepfake video forgery detection
Ganguly, S., Mohiuddin, S., Malakar, S., Cuevas, E., & Sarkar, R. (2022) · Pattern Analysis and Applications · doi:10.1007/s10044-022-01083-2 · Cited by 45 · see citing works
Narayan, K., Agarwal, H., Thakral, K., Mittal, S., Vatsa, M., & Singh, R. (2022) · 2022 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb54206.2022.10007968 · Cited by 26 · see citing works
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection
Zhu, H., Fu, C., Wu, Q., Wu, W., Qian, C., & He, R. (2020) · arXiv · doi:10.48550/arxiv.2011.02674 · Cited by 20 · see citing works
- Authors
- Cozzolino, D., Poggi, G., & Verdoliva, L.
- Affiliations
- University of Naples Federico II (IT)
- Venue
- Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security
- Publisher
- ACM
- Published
- 2017-06-20
- Pages
- 159-164
- OpenAlex
- W2603123944
- Cited by
- 349
- References
- 30
- Citations by year
- 2026: 12 · 2025: 39 · 2024: 32 · 2023: 53 · 2022: 67 · 2021: 61 · 2020: 50 · 2019: 17 · 2018: 16 · 2017: 2
- FWCI
- 6.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) (publishedVersion)
- Funders
- Defense Advanced Re search Projects Agency (FA8750-16-2-0204)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- fine-tuning, image forgery detection, image localization, forensic image analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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