SoK: Anti-Facial Recognition Technology
Emily Wenger, Shawn Shan, Haitao Zheng, Ben Y. Zhao
摘要
The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy. In response, a broad suite of so-called "anti-facial recognition" (AFR) tools has been developed to help users avoid unwanted facial recognition. The set of AFR tools proposed in the last few years is wide-ranging and rapidly evolving, necessitating a step back to consider the broader design space of AFR systems and long-term challenges. This paper aims to fill that gap and provides the first comprehensive analysis of the AFR research landscape. Using the operational stages of FR systems as a starting point, we create a systematic framework for analyzing the benefits and tradeoffs of different AFR approaches. We then consider both technical and social challenges facing AFR tools and propose directions for future research in this field.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper9
- Transferable Adversarial Facial Images for Privacy ProtectionMinghui Li, Jiangxiong Wang, Hao Zhang, Ziqi Zhou 等ACM MM 2024 · 被引用 11 次
- Protego: User-Centric Pose-Invariant Privacy Protection Against Face Recognition-Induced Digital Footprint ExposureZiling Wang, Shuya Yang, Jialin Lu, Ka-Ho ChowCVPR 2026 · 被引用 2 次
- Diffusion-based Adversarial Identity Manipulation for Facial Privacy ProtectionLiqin Wang, Qianyue Hu, Wei Lu, Xiangyang LuoACM MM 2025 · 被引用 1 次
- DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR StrategyWenshu Fan, Minxing Zhang, Hongwei Li, Wenbo Jiang 等CCS 2025 · 被引用 1 次
- Understanding the (In)Security of Cross-side Face Verification Systems in Mobile Apps: A System PerspectiveXiaohan Zhang, Haoqi Ye, Ziqi Huang, Xiao Ye 等S&P 2023
它引用的顶会 Paper25
- Towards Evaluating the Robustness of Neural NetworksNicholas Carlini, David A. WagnerS&P 2017 · 被引用 9,786 次
- Membership Inference Attacks Against Machine Learning ModelsReza Shokri, Marco Stronati, Congzheng Song, Vitaly ShmatikovS&P 2017 · 被引用 5,137 次
- Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face RecognitionMahmood Sharif, Sruti Bhagavatula, Lujo Bauer, Michael K. ReiterCCS 2016 · 被引用 1,765 次
- Trojaning Attack on Neural NetworksYingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee 等NDSS 2018 · 被引用 1,377 次
- MagNet: A Two-Pronged Defense against Adversarial ExamplesDongyu Meng, Hao ChenCCS 2017 · 被引用 1,295 次
相关 Paper
- LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial RecognitionValeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan 等ICLR 2021 · 被引用 52 次
- CamPro: Camera-based Anti-Facial RecognitionWenjun Zhu, Yuan Sun, Jiani Liu, Yushi Cheng 等NDSS 2024
- Fawkes: Protecting Privacy against Unauthorized Deep Learning ModelsShawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li 等USENIX Security 2020
- FACE-AUDITOR: Data Auditing in Facial Recognition SystemsMin Chen, Zhikun Zhang, Tianhao Wang, Michael Backes 等USENIX Security 2023
- FracFace: Breaking the Visual Clues - Fractal-Based Privacy-Preserving Face RecognitionWanying Dai, Beibei Li, Naipeng Dong, Guangdong Bai 等NeurIPS 2025 · 被引用 7 次
