mlsanityData to 14 Oct 2026

Personalized Face Privacy Protection From a Single Image

arXiv 2605.19032 ↗First posted 18 May 2026cs.CVcited by 0

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Abstract (arXiv)

Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources and intrude on personal privacy via unauthorized use of facial recognition models. This paper presents FaceCloak, a novel personalized face privacy protection system, which can generate defensive identity-specific universal face privacy masks from a single image of a user, causing facial recognition to fail. FaceCloak introduces a three-stage personalized face perturbation learning methodology: (1) It generates a small set of high-variety synthetic face images of a person based on a single image of the person. (2) It learns face cloaking by adding more protection to key facial-identity leakage regions through iterative perturbation generation over the small set of synthetic images, effectively shifting a user's identity embedding towards a distant anchor identity and away from a similar one. (3) It generates a personalized identity-protective mask in the form of pixel-wise cloaking, which is light-weight and can be efficiently applied to any facial image of a user while maintaining good perceptual quality. Extensive experiments on three popular face datasets across ten recognition models show the effectiveness of FaceCloak compared to 29 other existing representative methods. Code is available at https://github.com/zacharyyahn/FaceCloak

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 46 of its 55 references that are in the map. First posted on arXiv 18 May 2026.

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References · 46

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  5. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  6. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  7. 2022Generative Adversarial Networkscited by 5,627
  8. 2017Squeeze-and-Excitation Networkscited by 4,789
  9. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  10. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  11. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  12. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
34 more
  1. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 971
  2. 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
  3. 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 811
  4. 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 624
  5. 2019Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attackscited by 442
  6. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 337
  7. 2018MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devicescited by 182
  8. 2019AdvHat: Real-world adversarial attack on ArcFace Face ID systemcited by 163
  9. 2017Generative Adversarial Perturbationscited by 153
  10. 2024Arc2Face: A Foundation Model for ID-Consistent Human Facescited by 101
  11. 2021Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognitioncited by 97
  12. 2021LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognitioncited by 94
  13. 2018Generalizable Data-free Objective for Crafting Universal Adversarial Perturbationscited by 88
  14. 2022Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfercited by 78
  15. 2020Towards Face Encryption by Generating Adversarial Identity Maskscited by 63
  16. 2019AdvFaces: Adversarial Face Synthesiscited by 60
  17. 2017Level Playing Field for Million Scale Face Recognitioncited by 43
  18. 2023CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Searchcited by 43
  19. 2021Masked Face Recognition Challenge: The InsightFace Track Reportcited by 41
  20. 2023DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protectioncited by 35
  21. 2024DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protectioncited by 32
  22. 2024Transferable Adversarial Facial Images for Privacy Protectioncited by 27
  23. 2020Face-Off: Adversarial Face Obfuscationcited by 25
  24. 2022OPOM: Customized Invisible Cloak towards Face Privacy Protectioncited by 22
  25. 2022SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognitioncited by 21
  26. 2023Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Modelcited by 21
  27. 2021SoK: Anti-Facial Recognition Technologycited by 16
  28. 2025Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attackscited by 14
  29. 2020FoggySight: A Scheme for Facial Lookup Privacycited by 13
  30. 2020Fawkes: Protecting Privacy against Unauthorized Deep Learning Modelscited by 11
  31. 2024Personalized Privacy Protection Mask Against Unauthorized Facial Recognitioncited by 11
  32. 2024Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priorscited by 8
  33. 2023AdvCloak: Customized Adversarial Cloak for Privacy Protectioncited by 5
  34. 2024DIP-Watermark: A Double Identity Protection Method Based on Robust Adversarial Watermarkcited by 3

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