Haoyue Wang
ABSTRACT
Many online applications require the users to upload their identity documents for authentication. The fake identity document is one of the main threats which compromises the security and reliability of such online applications. Existing techniques focus on the detection of digitally forged identity documents, which neglect the impact of physical forgeries. In this paper, we look into the problem of detecting physically occluded fake identity documents, which can be easily generated without any image processing knowledge. We observe that the physical occlusions inevitably produce occluded boundaries on the document. To take the advantage, we propose an Occluded Boundary Representation Learning (OBRL) module to progressively learn the occluded boundary features. These are then fed into an Occluded Boundary Message Passing (OBMP) module to effectively diffuse the physical occlusion traces to enhance the backbone features for robust detection. We newly construct a Physically Occluded Fake ID Card image dataset (POID) for evaluation. Various experiments are conducted on the POID, where our scheme is able to achieve 99.6% of accuracy in detecting physically occluded fake ID card images with a mAP of over 85% to localize the occlusion regions.
- Musab Al-Ghadi, Zuheng Ming, Petra Gomez-Krämer, and Jean-Christophe Burie. 2022. Identity Documents Authentication based on Forgery Detection of Guilloche Pattern. arxiv: 2206.10989 [cs.CV]Google Scholar
- Diego Benalcazar, Juan E Tapia, Sergio Gonzalez, et al. 2023. Synthetic ID Card Image Generation for Improving Presentation Attack Detection. IEEE Transactions on Information Forensics and Security (2023).Google Scholar
- Romain Bertrand, Oriol Ramos Terrades, Petra Gomez-Krämer, Patrick Franco, and Jean-Marc Ogier. 2015. A conditional random field model for font forgery detection. In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 576--580.Google ScholarDigital Library
- Nabil Ghanmi, Cyrine Nabli, and Ahmad-Montaser Awal. 2021. CheckSim: A reference-based identity document verification by image similarity measure. In Document Analysis and Recognition-ICDAR 2021 Workshops: Lausanne, Switzerland, September 5-10, 2021, Proceedings, Part I 16. Springer, 422--436.Google ScholarDigital Library
- Sebastian Gonzalez and Juan Tapia. 2023. Improving Presentation Attack Detection for ID Cards on Remote Verification Systems. arxiv: 2301.09542 [cs.CV]Google Scholar
- Sebastian Gonzalez, Andres Valenzuela, and Juan Tapia. 2021. Hybrid two-stage architecture for tampering detection of chipless id cards. IEEE Transactions on Biometrics, Behavior, and Identity Science, Vol. 3, 1 (2021), 89--100.Google ScholarCross Ref
- Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision. 2961--2969.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
- Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, and Geguang Pu. 2020. Fakepolisher: Making deepfakes more detection-evasive by shallow reconstruction. In Proceedings of the 28th ACM international conference on multimedia. 1217--1226.Google ScholarDigital Library
- Ramesh Jain, Rangachar Kasturi, Brian G Schunck, et al. 1995. Machine vision. Vol. 5. McGraw-hill New York.Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. https://openreview.net/forum?id=SJU4ayYglGoogle Scholar
- Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, and Fang Wen. 2020a. Advancing High Fidelity Identity Swapping for Forgery Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
- Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. 2020b. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5001--5010.Google ScholarCross Ref
- Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision. 10012--10022.Google ScholarCross Ref
- Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In International Conference on Learning Representations.Google Scholar
- Ivan Perov, Daiheng Gao, Nikolay Chervoniy, Kunlin Liu, Sugasa Marangonda, Chris Umé, Mr Dpfks, Carl Shift Facenheim, Luis RP, Jian Jiang, et al. 2020. DeepFaceLab: Integrated, flexible and extensible face-swapping framework. arXiv preprint arXiv:2005.05535 (2020).Google Scholar
- Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. 2019. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF international conference on computer vision. 1--11.Google ScholarCross Ref
- Aziza Satkhozhina, Ildus Ahmadullin, and Jan P Allebach. 2013. Optical font recognition using conditional random field. In Proceedings of the 2013 ACM symposium on Document engineering. 119--122.Google ScholarDigital Library
- Steven Schwarcz and Rama Chellappa. 2021. Finding facial forgery artifacts with parts-based detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 933--942.Google ScholarCross Ref
- Shize Shang, Xiangwei Kong, and Xingang You. 2015. Document forgery detection using distortion mutation of geometric parameters in characters. Journal of Electronic Imaging, Vol. 24, 2 (2015), 023008--023008.Google ScholarCross Ref
- Ronan Sicre, Ahmad Montaser Awal, and Teddy Furon. 2017. Identity documents classification as an image classification problem. In Image Analysis and Processing-ICIAP 2017: 19th International Conference, Catania, Italy, September 11-15, 2017, Proceedings, Part II 19. Springer, 602--613.Google Scholar
- Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. PMLR, 6105--6114.Google Scholar
- Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. 2016. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2387--2395.Google ScholarDigital Library
- Liang Wu, Chengquan Zhang, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding, and Xiang Bai. 2019. Editing text in the wild. In Proceedings of the 27th ACM international conference on multimedia. 1500--1508.Google ScholarDigital Library
- Qian Yang, Jing Huang, and Weisi Lin. 2020. Swaptext: Image Based Texts Transfer in Scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14700--14709.Google ScholarCross Ref
- Baogen Zhang, Sheng Li, Guorui Feng, Zhenxing Qian, and Xinpeng Zhang. 2022. Patch Diffusion: a general module for face manipulation detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 3243--3251.Google ScholarCross Ref
- Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu. 2021b. Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2185--2194.Google ScholarCross Ref
- Lin Zhao, Chen Chen, and Jiwu Huang. 2021a. Deep learning-based forgery attack on document images. IEEE Transactions on Image Processing, Vol. 30 (2021), 7964--7979.Google ScholarCross Ref
- Xiangyu Zhu, Hao Wang, Hongyan Fei, Zhen Lei, and Stan Z Li. 2021. Face forgery detection by 3d decomposition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2929--2939.Google ScholarCross Ref
Index Terms
- On Physically Occluded Fake Identity Document Detection
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