mlsanityData to 14 Oct 2026

FIVA: Facial Image and Video Anonymization and Anonymization Defense

arXiv 2309.04228 ↗First posted 8 Sep 2023cs.CVcited by 12

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Abstract (arXiv)

In this paper, we present a new approach for facial anonymization in images and videos, abbreviated as FIVA. Our proposed method is able to maintain the same face anonymization consistently over frames with our suggested identity-tracking and guarantees a strong difference from the original face. FIVA allows for 0 true positives for a false acceptance rate of 0.001. Our work considers the important security issue of reconstruction attacks and investigates adversarial noise, uniform noise, and parameter noise to disrupt reconstruction attacks. In this regard, we apply different defense and protection methods against these privacy threats to demonstrate the scalability of FIVA. On top of this, we also show that reconstruction attack models can be used for detection of deep fakes. Last but not least, we provide experimental results showing how FIVA can even enable face swapping, which is purely trained on a single target image.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 27 of its 34 references that are in the map. First posted on arXiv 8 Sep 2023.

Builds on

27 of 34 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  5. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  6. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  7. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
  8. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  9. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,101
  10. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,098
  11. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 971
  12. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
15 more
  1. 2019FSGAN: Subject Agnostic Face Swapping and Reenactmentcited by 320
  2. 2021SimSwap: An Efficient Framework For High Fidelity Face Swappingcited by 243
  3. 2019DeepPrivacy: A Generative Adversarial Network for Face Anonymizationcited by 108
  4. 2021One Shot Face Swapping on Megapixelscited by 105
  5. 2021HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swappingcited by 92
  6. 2020CIAGAN: Conditional Identity Anonymization Generative Adversarial Networkscited by 88
  7. 2017On Face Segmentation, Face Swapping, and Face Perceptioncited by 71
  8. 2018Learning to Anonymize Faces for Privacy Preserving Action Detectioncited by 63
  9. 2022DeepPrivacy2: Towards Realistic Full-Body Anonymizationcited by 51
  10. 2019AnonymousNet: Natural Face De-Identification with Measurable Privacycited by 50
  11. 2019Live Face De-Identification in Videocited by 48
  12. 2022FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swappingcited by 44
  13. 2021FaceController: Controllable Attribute Editing for Face in the Wildcited by 22
  14. 2022My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymizationcited by 14
  15. 2021CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulationcited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 5.

12 papers.

  1. 2024Face Anonymization Made Simplecited by 18
  2. 2024Face De-identification: State-of-the-art Methods and Comparative Studiescited by 9
  3. 2025NullFace: Training-Free Localized Face Anonymizationcited by 4
  4. 2025Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunitiescited by 2
  5. 2025BLANKET: Anonymizing Faces in Infant Video Recordingscited by 2
  6. 2024FAKER: Full-body Anonymization with Human Keypoint Extraction for Real-time Video Deidentificationcited by 1
  7. 2026Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemescited by 1
  8. 2024Forbes: Face Obfuscation Rendering via Backpropagation Refinement Schemecited by 0
  9. 2026Self-Supervised Uncalibrated Multi-View Video Anonymization in the Operating Roomcited by 0
  10. 2026ReGenHuman: Re-Generating Human Appearances for Realistic Full-Body Video Anonymizationcited by 0
  11. 2026HYDRO: Towards Non-Reversible Face De-Identification Using a High-Fidelity Hybrid Diffusion and Target-Oriented Approachcited by 0
  12. 2026Adversarial Attacks and Identity Leakage in De-Identification Systems: An Empirical Studycited by 0