mlsanityData to 14 Oct 2026

Natural and Effective Obfuscation by Head Inpainting

arXiv 1711.09001 ↗First posted 24 Nov 2017cs.CV cs.CR cs.CYcited by 60

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Abstract (arXiv)

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. body pose) for seamless hypothesis of sensible head pose, and (2) facial landmark conditioned head inpainting. We verify that our inpainting method generates realistic person images, while achieving superior obfuscation performance against automatic person recognizers.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Nov 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 16; 2022: 11; 2023: 12; 2024: 10; 2025: 7; 2026: 4.

60 papers.

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  1. 2021A Study of Face Obfuscation in ImageNetcited by 83
  2. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  3. 2022DeepPrivacy2: Towards Realistic Full-Body Anonymizationcited by 51
  4. 2021Data Poisoning Won't Save You From Facial Recognitioncited by 45
  5. 2024Deep Learning-based Image and Video Inpainting: A Surveycited by 44
  6. 2023CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Searchcited by 43
  7. 2021Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Aheadcited by 41
  8. 2023Diff-Privacy: Diffusion-based Face Privacy Protectioncited by 26
  9. 2021IdentityDP: Differential Private Identification Protection for Face Imagescited by 24
  10. 2021DP-Image: Differential Privacy for Image Data in Feature Spacecited by 23
  11. 2023RiDDLE: Reversible and Diversified De-identification with Latent Encryptorcited by 21
  12. 2021Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semanticscited by 20
38 more
  1. 2024Face Anonymization Made Simplecited by 18
  2. 2021Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Frameworkcited by 17
  3. 2023Ethical Considerations for Responsible Data Curationcited by 17
  4. 2021Teacher Model Fingerprinting Attacks Against Transfer Learningcited by 16
  5. 2022Realistic Full-Body Anonymization with Surface-Guided GANscited by 15
  6. 2022My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymizationcited by 14
  7. 2021Differentially Private Imaging via Latent Space Manipulationcited by 13
  8. 2021CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulationcited by 12
  9. 2023Does Image Anonymization Impact Computer Vision Training?cited by 12
  10. 2021Graph-based Generative Face Anonymisation with Pose Preservationcited by 9
  11. 2023GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressionscited by 9
  12. 2024Face De-identification: State-of-the-art Methods and Comparative Studiescited by 9
  13. 2024View-consistent Object Removal in Radiance Fieldscited by 7
  14. 2021FICGAN: Facial Identity Controllable GAN for De-identificationcited by 6
  15. 2021A Review on Visual Privacy Preservation Techniques for Active and Assisted Livingcited by 6
  16. 2022Learnable Privacy-Preserving Anonymization for Pedestrian Imagescited by 6
  17. 2023Face Encryption via Frequency-Restricted Identity-Agnostic Attackscited by 6
  18. 2022Facial Expression Translation using Landmark Guided GANscited by 4
  19. 2024Video-to-Text Pedestrian Monitoring (VTPM): Leveraging Computer Vision and Large Language Models for Privacy-Preserve Pedestrian Activity Monitoring at Intersectionscited by 4
  20. 2025NullFace: Training-Free Localized Face Anonymizationcited by 4
  21. 2021Safe Fakes: Evaluating Face Anonymizers for Face Detectorscited by 3
  22. 2022A comprehensive survey on semantic facial attribute editing using generative adversarial networkscited by 3
  23. 2023Introducing Explicit Gaze Constraints to Face Swappingcited by 3
  24. 2022GMFIM: A Generative Mask-guided Facial Image Manipulation Model for Privacy Preservationcited by 2
  25. 2022Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacycited by 2
  26. 2022StyleGAN as a Utility-Preserving Face De-identification Methodcited by 2
  27. 2024My Body My Choice: Human-Centric Full-Body Anonymizationcited by 2
  28. 2025DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategycited by 2
  29. 2025BLANKET: Anonymizing Faces in Infant Video Recordingscited by 2
  30. 2026Now You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videoscited by 2
  31. 2021Privacy-preserving Object Detectioncited by 1
  32. 2022PrivPAS: A real time Privacy-Preserving AI System and applied ethicscited by 1
  33. 2024Towards mitigating uncann(eye)ness in face swaps via gaze-centric loss termscited by 1
  34. 2024Region of Interest Loss for Anonymizing Learned Image Compressioncited by 1
  35. 2024SemDP: Semantic-level Differential Privacy Protection for Face Datasetscited by 1
  36. 2022Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inversecited by 0
  37. 2023Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesiscited by 0
  38. 2023PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flowcited by 0

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