The proliferation of images captured from millions of cameras and the advancement of facial recognition (FR) technology have made the abuse of FR a severe privacy threat. Existing works typically rely on obfuscation, synthesis, or adversarial examples to modify faces in images to achieve anti-facial recognition (AFR). However, the unmodified images captured by camera modules that contain sensitive personally identifiable information (PII) could still be leaked. In this paper, we propose a novel approach, CamPro, to capture inborn AFR images. CamPro enables well-packed commodity camera modules to produce images that contain little PII and yet still contain enough information to support other non-sensitive vision applications, such as person detection. Specifically, CamPro tunes the configuration setup inside the camera image signal processor (ISP), i.e., color correction matrix and gamma correction, to achieve AFR, and designs an image enhancer to keep the image quality for possible human viewers. We implemented and validated CamPro on a proof-of-concept camera, and our experiments demonstrate its effectiveness on ten state-of-the-art black-box FR models. The results show that CamPro images can significantly reduce face identification accuracy to 0.3\% while having little impact on the targeted non-sensitive vision application. Furthermore, we find that CamPro is resilient to adaptive attackers who have re-trained their FR models using images generated by CamPro, even with full knowledge of privacy-preserving ISP parameters.
DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategy
University of Electronic Science and Technology of China（电子科技大学）；CISPA Helmholtz Center for Information Security（信息安全赫尔姆霍茨中心）；The Chinese University of Hong Kong（香港中文大学）
Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models
School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；China Mobile Information Technology Co., Ltd.（中国移动信息科技有限公司）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Center for Research on Intelligent Perception and Computing (CRIPAC), Institute of Automation Chinese Academy of Sciences (CASIA)（多模态人工智能系统国家重点实验室（MAIS）、智能感知与计算研究中心（CRIPAC）、中国科学院自动化研究所（CASIA））
Deep Learning Models for Robust Facial Liveness Detection
Department of Theoretical and Applied Sciences, eCampus University（eCampus大学理论与应用科学系）；Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V. N. Karazin Kharkiv National University（V. N. 卡拉津哈尔科夫国立大学计算机科学与人工智能学院智能软件系统与技术系）；Department of Political Sciences, Communication and International Relations, University of Macerata（马切拉塔大学政治学、传播与国际关系系）；Marche Polytechnic University（马尔凯理工大学）；Department Economics and Law, University of Macerata（马切拉塔大学经济与法律系）；Department of Information Engineering, Marche Polytechnic University（马尔凯理工大学信息工程系）
BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Great Bay University（大湾区大学）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）
Purify then Guide: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computing and Information Technology, Great Bay University（大湾区大学计算与信息科技学院）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞智能与信息处理重点实验室）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室）；Tencent Youtu Lab（腾讯优图实验室）；School of Psychology, Shanghai Jiao Tong University（上海交通大学心理学院）；Faculty of Applied Sciences, Macao Polytechnic University（澳门理工学院应用科学学院）；The Chinese University of Hong Kong（香港中文大学）
Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification
National Laboratory of Pattern Recognition, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；Center for Research on Intelligent Perception and Computing, CASIA（中国科学院自动化研究所智能感知与计算研究中心）；Center for Excellence in Brain Science and Intelligence Technology, CAS（中国科学院脑科学与智能技术卓越创新中心）；University of Chinese Academy of Sciences（中国科学院大学）
G$^2$V$^2$former: Graph Guided Video Vision Transformer for Face Anti-Spoofing
University of Science and Technology of China（中国科学技术大学）；Great Bay University（大湾区大学）；Anhui Tsinglink Information Tech. Com Ltd.（安徽清link信息技术有限公司）；South China University of Technology（华南理工大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection
University of British Columbia（英属哥伦比亚大学）；Department of Statistics, University of British Columbia（英属哥伦比亚大学统计系）；Department of Electrical and Computer Engineering, University of British Columbia（英属哥伦比亚大学电气与计算机工程系）；School of Information and Communications Engineering, Xi’an Jiaotong University（西安交通大学信息与通信工程学院）
