mlsanityData to 7 Oct 2026

My Body My Choice: Human-Centric Full-Body Anonymization

arXiv 2406.09553 ↗First posted 13 Jun 2024cs.CV cs.AIcited by 2

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Abstract (arXiv)

In an era of increasing privacy concerns for our online presence, we propose that the decision to appear in a piece of content should only belong to the owner of the body. Although some automatic approaches for full-body anonymization have been proposed, human-guided anonymization can adapt to various contexts, such as cultural norms, personal relations, esthetic concerns, and security issues. ''My Body My Choice'' (MBMC) enables physical and adversarial anonymization by removal and swapping approaches aimed for four tasks, designed by single or multi, ControlNet or GAN modules, combining several diffusion models. We evaluate anonymization on seven datasets; compare with SOTA inpainting and anonymization methods; evaluate by image, adversarial, and generative metrics; and conduct reidentification experiments.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 38 of its 56 references that are in the map. First posted on arXiv 13 Jun 2024.

Builds on

38 of 56 references.

References · 38

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26 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

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Close in wording, no citation either way

  1. 2024Context-Aware Full Body Anonymization using Text-to-Image Diffusion Models
  2. 2026ReGenHuman: Re-Generating Human Appearances for Realistic Full-Body Video Anonymization
  3. 2025Reverse Personalization
  4. 2025Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Models
  5. 2025Controllable Localized Face Anonymization Via Diffusion Inpainting
  6. 2024Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data
  7. 2024Latent Diffusion Models for Attribute-Preserving Image Anonymization
  8. 2025Facial Attribute Based Text Guided Face Anonymization