mlsanityData to 7 Oct 2026

SoK: Anti-Facial Recognition Technology

arXiv 2112.04558 ↗First posted 8 Dec 2021cs.CR cs.CV cs.LGcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 58 of its 160 references that are in the map. First posted on arXiv 8 Dec 2021.

Builds on

58 of 160 references.

References · 58

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38 more
  1. 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
  2. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  3. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
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  5. 2018Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Modelscited by 362
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  13. 2017EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examplescited by 154
  14. 2019Transferable Clean-Label Poisoning Attacks on Deep Neural Netscited by 129
  15. 2019DeepPrivacy: A Generative Adversarial Network for Face Anonymizationcited by 108
  16. 2021LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognitioncited by 92
  17. 2020Radioactive data: tracing through trainingcited by 67
  18. 2020Towards Face Encryption by Generating Adversarial Identity Maskscited by 62
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  21. 2017MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examplescited by 46
  22. 2021Data Poisoning Won't Save You From Facial Recognitioncited by 45
  23. 2020Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Studycited by 40
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50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 4; 2025: 1; 2026: 5.

16 papers.

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  1. 2023CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Searchcited by 42
  2. 2023FACE-AUDITOR: Data Auditing in Facial Recognition Systemscited by 15
  3. 2024Personalized Privacy Protection Mask Against Unauthorized Facial Recognitioncited by 10
  4. 2024Face De-identification: State-of-the-art Methods and Comparative Studiescited by 9
  5. 2024Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priorscited by 7
  6. 2023CamPro: Camera-based Anti-Facial Recognitioncited by 2
  7. 2024Combining Machine Learning Defenses without Conflictscited by 2
  8. 2023Group-based Robustness: A General Framework for Customized Robustness in the Real Worldcited by 1
  9. 2026Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attackscited by 1
  10. 2022Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillancecited by 0
  11. 2023PrivacyGAN: robust generative image privacycited by 0
  12. 2025Controllable Adversarial Makeup for Privacy via Text-Guided Diffusioncited by 0
4 more
  1. 2026Personalized Face Privacy Protection From a Single Imagecited by 0
  2. 2026AI Security Research Should Better Incentivize Defense Researchcited by 0
  3. 2026DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Modelscited by 0
  4. 2026Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecyclecited by 0