mlsanityData to 7 Oct 2026

Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective

arXiv 1703.09471 ↗First posted 28 Mar 2017cs.CV cs.CR cs.GTcited by 38

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Abstract (arXiv)

Users like sharing personal photos with others through social media. At the same time, they might want to make automatic identification in such photos difficult or even impossible. Classic obfuscation methods such as blurring are not only unpleasant but also not as effective as one would expect. Recent studies on adversarial image perturbations (AIP) suggest that it is possible to confuse recognition systems effectively without unpleasant artifacts. However, in the presence of counter measures against AIPs, it is unclear how effective AIP would be in particular when the choice of counter measure is unknown. Game theory provides tools for studying the interaction between agents with uncertainties in the strategies. We introduce a general game theoretical framework for the user-recogniser dynamics, and present a case study that involves current state of the art AIP and person recognition techniques. We derive the optimal strategy for the user that assures an upper bound on the recognition rate independent of the recogniser's counter measure. Code is available at https://goo.gl/hgvbNK.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 7; 2023: 8; 2024: 8; 2025: 4; 2026: 2.

38 papers.

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  1. 2021A Study of Face Obfuscation in ImageNetcited by 83
  2. 2021Data Poisoning Won't Save You From Facial Recognitioncited by 45
  3. 2023CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Searchcited by 42
  4. 2023Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compressioncited by 39
  5. 2024Transferable Adversarial Facial Images for Privacy Protectioncited by 26
  6. 2021IdentityDP: Differential Private Identification Protection for Face Imagescited by 24
  7. 2021DP-Image: Differential Privacy for Image Data in Feature Spacecited by 23
  8. 2022OPOM: Customized Invisible Cloak towards Face Privacy Protectioncited by 22
  9. 2023Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspectivecited by 19
  10. 2023The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attackscited by 15
  11. 2022My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymizationcited by 14
  12. 2021Get a Model! Model Hijacking Attack Against Machine Learning Modelscited by 13
26 more
  1. 2023Does Image Anonymization Impact Computer Vision Training?cited by 12
  2. 2021Going Grayscale: The Road to Understanding and Improving Unlearnable Examplescited by 10
  3. 2024Face De-identification: State-of-the-art Methods and Comparative Studiescited by 9
  4. 2021Two-Face: Adversarial Audit of Commercial Face Recognition Systemscited by 8
  5. 2022Hiding Visual Information via Obfuscating Adversarial Perturbationscited by 7
  6. 2024Enhancing targeted transferability via feature space fine-tuningcited by 7
  7. 2024Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priorscited by 7
  8. 2025Diffusion-based Adversarial Identity Manipulation for Facial Privacy Protectioncited by 5
  9. 2021Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attackscited by 4
  10. 2022DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detectioncited by 4
  11. 2024PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Servicescited by 4
  12. 2023Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacycited by 3
  13. 2024Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustnesscited by 3
  14. 2024Learning Robust and Privacy-Preserving Representations via Information Theorycited by 3
  15. 2022GMFIM: A Generative Mask-guided Facial Image Manipulation Model for Privacy Preservationcited by 2
  16. 2022StyleGAN as a Utility-Preserving Face De-identification Methodcited by 2
  17. 2023Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Taskscited by 2
  18. 2025DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategycited by 2
  19. 2026Machine Pareidolia: Protecting Facial Image with Emotional Editingcited by 1
  20. 2021Deep Learning Approach Protecting Privacy in Camera-Based Critical Applicationscited by 0
  21. 2022Learning Location from Shared Elevation Profiles in Fitness Apps: A Privacy Perspectivecited by 0
  22. 2023PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flowcited by 0
  23. 2024Mask-up: Investigating Biases in Face Re-identification for Masked Facescited by 0
  24. 2025Controllable Adversarial Makeup for Privacy via Text-Guided Diffusioncited by 0
  25. 2025DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protectioncited by 0
  26. 2026Asymmetric Invertible Threat: Learning Reversible Privacy Defense for Face Recognitioncited by 0