Menglu Wang
Jiawei Liu
ABSTRACT
Image forgery localization, which aims to find suspicious regions tampered with splicing, copy-move or removal manipulations, has attracted increasing attention. Existing image forgery localization methods have made great progress on public datasets. However, these methods suffer a severe performance drop when the forged images are JPEG compressed, which is widely applied in social media transmission. To tackle this issue, we propose a wavelet-based compression representation learning scheme for the specific JPEG-resistant image forgery localization. Specifically, to improve the performance against JPEG compression, we first learn the abstract representations to distinguish various compression levels through wavelet integrated contrastive learning strategy. Then, based on the learned representations, we introduce a JPEG compression-aware image forgery localization network to flexibly handle forged images compressed with various JPEG quality factors. Moreover, a boundary correction branch is designed to alleviate the edge artifacts caused by JPEG compression. Extensive experiments demonstrate the superiority of our method to existing state-of-the-art approaches, not only on standard datasets, but also on the JPEG forged images with multiple compression quality factors.
Supplemental Material
- Edoardo Ardizzone, Alessandro Bruno, and Giuseppe Mazzola. 2010. Copy-move forgery detection via texture description. In Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence. 59--64.Google Scholar
Digital Library
- Belhassen Bayar and Matthew C Stamm. 2018. Constrained convolutional neural networks: A new approach towards general purpose image manipulation detection. IEEE Transactions on Information Forensics and Security, Vol. 13, 11 (2018), 2691--2706.Google ScholarCross Ref
- Shenhao Cao, Qin Zou, Xiuqing Mao, Dengpan Ye, and Zhongyuan Wang. 2021. Metric Learning for Anti-Compression Facial Forgery Detection. In Proceedings of the 29th ACM International Conference on Multimedia. 1929--1937.Google ScholarDigital Library
- Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.Google Scholar
- Xinru Chen, Chengbo Dong, Jiaqi Ji, Juan Cao, and Xirong Li. 2021. Image Manipulation Detection by Multi-View Multi-Scale Supervision. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 14185--14193.Google ScholarCross Ref
- Vincent Christlein, Christian Riess, Johannes Jordan, Corinna Riess, and Elli Angelopoulou. 2012. An evaluation of popular copy-move forgery detection approaches. IEEE Transactions on information forensics and security, Vol. 7, 6 (2012), 1841--1854.Google ScholarDigital Library
- Davide Cozzolino, Diego Gragnaniello, and Luisa Verdoliva. 2014a. Image forgery detection through residual-based local descriptors and block-matching. In 2014 IEEE international conference on image processing (ICIP). IEEE, 5297--5301.Google ScholarCross Ref
- Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva. 2014b. Copy-move forgery detection based on patchmatch. In 2014 IEEE international conference on image processing (ICIP). IEEE, 5312--5316.Google ScholarCross Ref
- Davide Cozzolino and Luisa Verdoliva. 2019. Noiseprint: a CNN-based camera model fingerprint. IEEE Transactions on Information Forensics and Security, Vol. 15 (2019), 144--159.Google ScholarCross Ref
- Jing Dong, Wei Wang, and Tieniu Tan. 2013. Casia image tampering detection evaluation database. In 2013 IEEE China Summit and International Conference on Signal and Information Processing. IEEE, 422--426.Google ScholarCross Ref
- Max Ehrlich, Larry Davis, Ser-Nam Lim, and Abhinav Shrivastava. 2021. Analyzing and Mitigating JPEG Compression Defects in Deep Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2357--2367.Google ScholarCross Ref
- Jessica Fridrich and Jan Kodovsky. 2012. Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, Vol. 7, 3 (2012), 868--882.Google ScholarDigital Library
- Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. 2019. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 3146--3154.Google ScholarCross Ref
- Xueyang Fu, Menglu Wang, Xiangyong Cao, Xinghao Ding, and Zheng-Jun Zha. 2021a. A model-driven deep unfolding method for jpeg artifacts removal. IEEE Transactions on Neural Networks and Learning Systems (2021).Google ScholarCross Ref
- Xueyang Fu, Xi Wang, Aiping Liu, Junwei Han, and Zheng-Jun Zha. 2021b. Learning dual priors for jpeg compression artifacts removal. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4086--4095.Google ScholarCross Ref
- Haiying Guan, Mark Kozak, Eric Robertson, Yooyoung Lee, Amy N Yates, Andrew Delgado, Daniel Zhou, Timothee Kheyrkhah, Jeff Smith, and Jonathan Fiscus. 2019. MFC datasets: Large-scale benchmark datasets for media forensic challenge evaluation. In 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). IEEE, 63--72.Google ScholarCross Ref
- Jing Hao, Zhixin Zhang, Shicai Yang, Di Xie, and Shiliang Pu. 2021. TransForensics: Image Forgery Localization with Dense Self-Attention. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 15055--15064.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.Google ScholarCross Ref
- J Hsu and SF Chang. 2006. Columbia uncompressed image splicing detection evaluation dataset. Columbia DVMM Research Lab (2006).Google Scholar
- Jie Huang, Xueyang Fu, Zeyu Xiao, Feng Zhao, and Zhiwei Xiong. 2022a. Low-Light Stereo Image Enhancement. IEEE Transactions on Multimedia (2022).Google ScholarDigital Library
- Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, and Zhiwei Xiong. 2022b. Exposure Normalization and Compensation for Multiple-Exposure Correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6043--6052.Google ScholarCross Ref
- Jie Huang, Zhiwei Xiong, Xueyang Fu, Dong Liu, and Zheng-Jun Zha. 2019. Hybrid image enhancement with progressive Laplacian enhancing unit. In Proceedings of the 27th ACM International Conference on Multimedia. 1614--1622.Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. Computer science (2014).Google Scholar
- Haodong Li and Jiwu Huang. 2019. Localization of deep inpainting using high-pass fully convolutional network. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 8301--8310.Google ScholarCross Ref
- Haodong Li, Weiqi Luo, Xiaoqing Qiu, and Jiwu Huang. 2017. Image forgery localization via integrating tampering possibility maps. IEEE Transactions on Information Forensics and Security, Vol. 12, 5 (2017), 1240--1252.Google ScholarDigital Library
- Siwei Lyu, Xunyu Pan, and Xing Zhang. 2014. Exposing region splicing forgeries with blind local noise estimation. IJCV, Vol. 110, 2 (2014), 202--221.Google ScholarDigital Library
- Xunyu Pan and Siwei Lyu. 2010. Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security, Vol. 5, 4 (2010), 857--867.Google ScholarDigital Library
- Xunyu Pan, Xing Zhang, and Siwei Lyu. 2012. Exposing image splicing with inconsistent local noise variances. In 2012 IEEE International Conference on Computational Photography (ICCP). IEEE, 1--10.Google ScholarCross Ref
- Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. 2022. Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17389--17398.Google ScholarCross Ref
- Yuan Rao and Jiangqun Ni. 2021. Self-Supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 15034--15043.Google ScholarCross Ref
- Yuan Rao, Jiangqun Ni, and Huimin Zhao. 2020. Deep learning local descriptor for image splicing detection and localization. IEEE Access, Vol. 8 (2020), 25611--25625.Google ScholarCross Ref
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, Vol. 28 (2015), 91--99.Google Scholar
- Seung-Jin Ryu, Matthias Kirchner, Min-Jeong Lee, and Heung-Kyu Lee. 2013. Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Transactions on Information Forensics and Security, Vol. 8, 8 (2013), 1355--1370.Google ScholarDigital Library
- Ronald Salloum, Yuzhuo Ren, and C-C Jay Kuo. 2018. Image splicing localization using a multi-task fully convolutional network (MFCN). Journal of Visual Communication and Image Representation, Vol. 51 (2018), 201--209.Google ScholarCross Ref
- Luisa Verdoliva, Davide Cozzolino, and Giovanni Poggi. 2014. A feature-based approach for image tampering detection and localization. In 2014 IEEE international workshop on information forensics and security (WIFS). IEEE, 149--154.Google ScholarCross Ref
- Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, and Yulan Guo. 2021b. Unsupervised Degradation Representation Learning for Blind Super-Resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10581--10590.Google ScholarCross Ref
- Menglu Wang, Xueyang Fu, Zepei Sun, and Zheng-Jun Zha. 2021a. JPEG artifacts removal via compression quality ranker-guided networks. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 566--572.Google ScholarDigital Library
- Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. 2022. Uformer: A general u-shaped transformer for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17683--17693.Google ScholarCross Ref
- Bihan Wen, Ye Zhu, Ramanathan Subramanian, Tian-Tsong Ng, Xuanjing Shen, and Stefan Winkler. 2016. COVERAGE-A novel database for copy-move forgery detection. In 2016 IEEE international conference on image processing (ICIP). IEEE, 161--165.Google ScholarCross Ref
- Yue Wu, Wael Abd-Almageed, and Prem Natarajan. 2017. Deep matching and validation network: An end-to-end solution to constrained image splicing localization and detection. In Proceedings of the 25th ACM international conference on Multimedia. 1480--1502.Google ScholarDigital Library
- Yue Wu, Wael AbdAlmageed, and Premkumar Natarajan. 2019. Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9543--9552.Google ScholarCross Ref
- Ruikang Xu, Mingde Yao, Chang Chen, Lizhi Wang, and Zhiwei Xiong. 2021. Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation. arXiv preprint arXiv:2112.13003 (2021).Google Scholar
- Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, and Kayhan Batmanghelich. 2022. Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18311--18320.Google ScholarCross Ref
- Mingde Yao, Zhiwei Xiong, Lizhi Wang, Dong Liu, and Xuejin Chen. 2019. Spectral-depth imaging with deep learning based reconstruction. Optics express, Vol. 27, 26 (2019), 38312--38325.Google Scholar
- Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. 2018. Learning a discriminative feature network for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1857--1866.Google ScholarCross Ref
- Yingchen Yu, Fangneng Zhan, Shijian Lu, Jianxiong Pan, Feiying Ma, Xuansong Xie, and Chunyan Miao. 2021. WaveFill: A Wavelet-based Generation Network for Image Inpainting. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 14114--14123.Google ScholarCross Ref
- Hai-Dong Yuan. 2011. Blind forensics of median filtering in digital images. IEEE Transactions on Information Forensics and Security, Vol. 6, 4 (2011), 1335--1345.Google ScholarDigital Library
- Xudong Zhao, Shilin Wang, Shenghong Li, and Jianhua Li. 2014. Passive image-splicing detection by a 2-D noncausal Markov model. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 25, 2 (2014), 185--199.Google ScholarDigital Library
- Xudong Zhao, Shilin Wang, Shenghong Li, Jianhua Li, and Quanqiao Yuan. 2013. Image splicing detection based on noncausal Markov model. In 2013 IEEE International Conference on Image Processing. IEEE, 4462--4466.Google ScholarCross Ref
- Peng Zhou, Bor-Chun Chen, Xintong Han, Mahyar Najibi, Abhinav Shrivastava, Ser-Nam Lim, and Larry Davis. 2020. Generate, segment, and refine: Towards generic manipulation segmentation. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 13058--13065.Google ScholarCross Ref
- Peng Zhou, Xintong Han, Vlad I Morariu, and Larry S Davis. 2018. Learning rich features for image manipulation detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1053--1061.Google ScholarCross Ref
Index Terms
- JPEG Compression-aware Image Forgery Localization
Recommendations
A novel fast self-restoration semi-fragile watermarking algorithm for image content authentication resistant to JPEG compression
IWDW'11: Proceedings of the 10th international conference on Digital-Forensics and WatermarkingIn the past few years, semi-fragile watermarking has become increasingly important to verify the content of images and localise the tampered areas, while tolerating some non-malicious manipulations. Moreover, some researchers proposed self-restoration ...
Self-embedding watermarking scheme against JPEG compression with superior imperceptibility
To improve the imperceptibility, security and tamper detection performance, a self-embedding watermarking scheme against JPEG compression is proposed in this work. The recovery watermark of all blocks in the host image, which consists of 6-bit DC-code ...
Blind image steganography algorithm development which resistant against JPEG compression attack
AbstractDigital image steganography is now one of the effective methods for exchanging confidential information over the public network. One of the major challenges is to protect secret data embedded in to image against JPEG compression. This paper ...
Comments