Semantic-agnostic progressive subtractive network for image manipulation detection and localization

J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner, Face2face: Real-time face capture and reenactment of rgb videos, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR2016), pp. 2387–2395. Yang, 2022, CSDM: A cross-scale decomposition method for low-light image enhancement, Signal Process., 202 T.M. Ghanim, and A.M. Nabil, Offline signature verification and forgery detection approach, in Proc. 13th Int. Conf. Comput. Eng. Syst. (ICCES2018), pp. 293–298. Verdoliva, 2020, Media forensics and deepfakes: An overview, IEEE J. Select. Top. Signal Process., 14, 910, 10.1109/JSTSP.2020.3002101 Jiang, 2022, Forgery-free signature verification with stroke-aware cycle-consistent generative adversarial network, Neurocomputing., 507, 345, 10.1016/j.neucom.2022.08.017 L. Abady, G. Dimitri, M. Barni, Detection and localization of gan manipulated multi-spectral satellite images, in Proceedings of the ESANN, (2022). Kaushik, 2023, Forensics in medical imaging: techniques and tools, Unleashing the Art of Digital Forensics, Chapman and Hall/CRC, 165 Li, 2023, Recaptured screen image identification based on vision transformer, J. Vis. Commun. Image Represent., 90, 10.1016/j.jvcir.2022.103692 El-Bendary, 2023, An efficient hidden marking approach for forensic and contents verification of digital images, Multimedia Tools Appl., 1–32 Y. Wu, W. Abd-Almageed, and P. Natarajan, Image copy-move forgery detection via an end-to-end deep neural network, in IEEE Winter Conference on Applications of Computer Vision (WACV2018), pp. 1907–1915. M. Huh, A. Liu, A. Owens, and A.A. Efros, Fighting fake news: Image splice detection via learned self-consistency, in Proc. Eur. Conf. Comput. Vis. (ECCV2018), pp. 101–117. H. Dang, F. Liu, J. Stehouwer, X. Liu, and A.K. Jain, On the detection of digital face manipulation, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR2020), pp. 5781–5790. Wang, 2021, Adversarial analysis for source camera identification, IEEE Trans. Circuits Syst. Video Technol., 31, 4174, 10.1109/TCSVT.2020.3047084 Zhou, 2020, Generate, segment, and refine: Towards generic manipulation segmentation, AAAI – AAAI Conf. Artif. Intell., 34, 13058 J.H. Bappy, A. Roy-Chowdhury, J. Bunk, L. Nataraj, and B.S. Manjunath, Exploiting spatial structure for localizing manipulated image regions, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV2017), pp. 4980–4989. Bappy, 2019, Hybrid lstm and encoder–decoder architecture for detection of image forgeries, IEEE Trans. Image Process., 28, 3286, 10.1109/TIP.2019.2895466 D. Zhang, H. Zhang, J. Tang, X.S. Hua, and Q. Sun, Causal intervention for weakly-supervised semantic segmentation, Advances in Neural Information Processing Systems (NeurIPS2022), 33 (2022) 655–666. D. Zhang, H. Zhang, J. Tang, X.S. Hua, and Q. Sun, Self-regulation for semantic segmentation, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV2021), pp. 6953–6963. Liu, 2020, Locating splicing forgery by adaptive-SVD noise estimation and vicinity noise descriptor, Neurocomputing, 387, 172, 10.1016/j.neucom.2019.12.105 P. Zhou, X. Han, V. Morariu, and L. Davis, Learning rich features for image manipulation detection, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR2018), pp. 1053–1061. Salloum, 2018, Image splicing localization using a multi-task fully convolutional network (mfcn), J. Visual Commun. Image Represent, 51, 201, 10.1016/j.jvcir.2018.01.010 Zhang, 2022, Multi-task SE-Network for image splicing localization, IEEE Trans. Circuits Syst. Video Technol., 32, 4828, 10.1109/TCSVT.2021.3123829 Rao, 2021, Multi-semantic CRF-based attention model for image forgery detection and localization, Signal Process., 183, 10.1016/j.sigpro.2021.108051 X. Bi, Y. Wei, B. Xiao, W. Li, RRU-Net: The ringed residual U-Net for image splicing forgery detection, in IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW2019), pp. 30–39. X. Chen, C. Dong, J. Ji, J. Cao, and X. Li, Image manipulation detection by multi-view multi-scale supervision, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV2021), pp. 14185–14193. Liu, 2022 Ganapathi, 2022, Learning to localize image forgery using end-to-end attention network, Neurocomputing, 10.1016/j.neucom.2022.09.060 Krawetz, 2017, A picture’s worth, Hacker Factor, Solutions, 6, 1 Mahdian, 2009, Using noise inconsistencies for blind image forensics, Image Vision Comput., 27, 1497, 10.1016/j.imavis.2009.02.001 Ferrara, 2012, Image forgery localization via fine-grained analysis of CFA artifacts, IEEE Trans. Inf. Forensics Secur., 7, 1566, 10.1109/TIFS.2012.2202227 Shi, 2020, Global semantic consistency network for image manipulation detection, IEEE Signal Process Lett., 27, 1755, 10.1109/LSP.2020.3026954 J. Hao, Z. Zhang, S. Yang, D. Xie, S. Pu, TransForensics: Image forgery localization with dense self-attention, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV2021), pp. 15055–15064. Aria, 2022, QDL-CMFD: a quality-independent and deep learning-based Copy-Move image forgery detection method, Neurocomputing, 10.1016/j.neucom.2022.09.017 Hosny, 2022, An efficient CNN model to detect copy-move image forgery, IEEE Access., 10, 48622, 10.1109/ACCESS.2022.3172273 M. Kaya, K.J. Sani, S. Karakuş, Copy-Move forgery detection in digital forensic images using CNN, in 7th International Conference on Computer Science and Engineering (UBMK2022), pp. 239–245. Li, 2022, Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN, Sci. Rep., 12, 14987, 10.1038/s41598-022-19325-y Y. Wu, W. AbdAlmageed, P. Natarajan, Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR2019), pp. 9543–9552. H. Li, J. Huang, Localization of deep inpainting using high-pass fully convolutional network, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV2019), pp. 8301–8310. F.F. Niloy, K.K. Bhaumik, S.S. Woo, CFL-Net: Image forgery localization using contrastive learning, in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, (2023) 4642–4651. Lin, 2023, Image manipulation detection by multiple tampering traces and edge artifact enhancement, Pattern Recognit., 133, 10.1016/j.patcog.2022.109026 Wang, 2023 Y. Li, X. Chen, Z. Zhu, L. Xie, G. Huang, D. Du, and X. Wang, Attention-guided unified network for panoptic segmentation, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR2019), pp. 7026–7035. Yin, 2022, Contrastive learning based multi-task network for image manipulation detection, Signal Process., 201, 10.1016/j.sigpro.2022.108709 Zhuo, 2022, Self-adversarial training incorporating forgery attention for image forgery localization, IEEE Trans. Inf. Forensics Secur., 17, 819, 10.1109/TIFS.2022.3152362 Guo, 2021, Fake face detection via adaptive manipulation traces extraction network, Comput. Vision Image Understanding, 204, 10.1016/j.cviu.2021.103170 K. Simonyan, and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556. (2014) [Online]. Available: https://arxiv.org/abs/1409.1556. J. Dong, W. Wang, T. Tan, Casia image tampering detection evaluation database, in IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP2013), pp. 422–426. B. Wen, Y. Zhu, R. Subramanian, T.T. Ng, S. Winkler, A novel database for copy–move forgery detection, in Int. Conf. Image Process. (2016) 161–165. Y.-F. Hsu, and S.-F. Chang, Detecting image splicing using geometry invariants and camera characteristics consistency, in IEEE International Conference on Multimedia and Expo., (2006) 549–552. A. Novozamsky, B. Mahdian, S. Saic, IMD2020: A large–scale annotated dataset tailored for detecting manipulated images, in IEEE Winter Applications of Computer Vision Workshops (WACVW2020), pp. 71–80. NIST: Nimble media forensics challenge datasets, (2016) [Online]. Available:https://www.nist.gov/itl/iad/mig/media-forensicschallenge. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library, in Conference on Neural Information Processing Systems (NeurIPS2019). D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv:1412.6980. (2014) [Online]. Available: https://arxiv.org/abs/1412.6980.