mlsanityData to 7 Oct 2026

CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

arXiv 2005.09544 ↗First posted 19 May 2020cs.CVcited by 88

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Abstract (arXiv)

The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results.

Key facts. Cited by 88 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 13; 2023: 17; 2024: 13; 2025: 12; 2026: 15.

88 papers.

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  1. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  2. 2021Trustworthy AI: From Principles to Practicescited by 206
  3. 2023Emu: Generative Pretraining in Multimodalitycited by 156
  4. 2021Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localizationcited by 112
  5. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  6. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  7. 2021OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wildcited by 50
  8. 2021Data Poisoning Won't Save You From Facial Recognitioncited by 45
  9. 2021The 2021 Image Similarity Dataset and Challengecited by 44
  10. 2023Attribute-preserving Face Dataset Anonymization via Latent Code Optimizationcited by 43
  11. 2023Diff-Privacy: Diffusion-based Face Privacy Protectioncited by 25
  12. 2021IdentityDP: Differential Private Identification Protection for Face Imagescited by 24
38 more
  1. 2023LDFA: Latent Diffusion Face Anonymization for Self-driving Applicationscited by 21
  2. 2023RiDDLE: Reversible and Diversified De-identification with Latent Encryptorcited by 21
  3. 2021Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semanticscited by 20
  4. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  5. 2024G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priorscited by 20
  6. 2022GAN-based Facial Attribute Manipulationcited by 16
  7. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  8. 2022Realistic Full-Body Anonymization with Surface-Guided GANscited by 15
  9. 2022My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymizationcited by 14
  10. 2021CoPE: Conditional image generation using Polynomial Expansionscited by 12
  11. 2021CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulationcited by 12
  12. 2023FIVA: Facial Image and Video Anonymization and Anonymization Defensecited by 12
  13. 2022Unsupervised Text Deidentificationcited by 11
  14. 2022StyleID: Identity Disentanglement for Anonymizing Facescited by 10
  15. 2021Graph-based Generative Face Anonymisation with Pose Preservationcited by 9
  16. 2023GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressionscited by 9
  17. 2023Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunitiescited by 9
  18. 2024Face De-identification: State-of-the-art Methods and Comparative Studiescited by 9
  19. 2021A Systematical Solution for Face De-identificationcited by 8
  20. 2021On Generating Identifiable Virtual Facescited by 8
  21. 2023Disguise without Disruption: Utility-Preserving Face De-Identificationcited by 8
  22. 2021Temporally coherent video anonymization through GAN inpaintingcited by 7
  23. 2024Latent Diffusion Models for Attribute-Preserving Image Anonymizationcited by 7
  24. 2022Learnable Privacy-Preserving Anonymization for Pedestrian Imagescited by 6
  25. 2021Anonymization for Skeleton Action Recognitioncited by 5
  26. 2022MaSS: Multi-attribute Selective Suppressioncited by 5
  27. 2023Anonymizing medical case-based explanations through disentanglementcited by 5
  28. 2022Fantomas: Understanding Face Anonymization Reversibilitycited by 4
  29. 2024Medical Manifestation-Aware De-Identificationcited by 4
  30. 2025NullFace: Training-Free Localized Face Anonymizationcited by 4
  31. 2021Safe Fakes: Evaluating Face Anonymizers for Face Detectorscited by 3
  32. 2023k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignmentcited by 3
  33. 2022Robust Deepfake On Unrestricted Media: Generation And Detectioncited by 2
  34. 2022StyleGAN as a Utility-Preserving Face De-identification Methodcited by 2
  35. 2024RID-TWIN: An end-to-end pipeline for automatic face de-identification in videoscited by 2
  36. 2025BLANKET: Anonymizing Faces in Infant Video Recordingscited by 2
  37. 2026A2-Edit: Precise Reference-Guided Image Editing of Arbitrary Objects and Ambiguous Maskscited by 2
  38. 2022Invertible Mask Network for Face Privacy-Preservingcited by 1

50 most cited of 88 All 88 (JSON)