Summary of Context-aware Full Body Anonymization Using Text-to-image Diffusion Models, by Pascal Zwick et al.
Context-Aware Full Body Anonymization using Text-to-Image Diffusion Models
by Pascal Zwick, Kevin Roesch, Marvin Klemp, Oliver Bringmann
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for full-body person anonymization in real-world datasets is proposed, utilizing Stable Diffusion as a generative backend. This technique aims to balance privacy protection with retaining important features, such as facial recognition, while reducing the ability to recognize individuals by their hairstyle or clothing. The method outperforms state-of-the-art anonymization pipelines in terms of image quality, resolution, Inception Score (IS), and Frechet Inception Distance (FID). This workflow is invariant with respect to the image generator and can be used with the latest models available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Protecting personal information while keeping important features in real-world datasets is crucial. A new method for full-body person anonymization uses Stable Diffusion, a text-to-image diffusion model that creates photorealistic images from text prompts. This approach aims to balance privacy and facial recognition, making it useful for applications like self-driving cars. The method outperforms current methods in terms of image quality and is flexible enough to work with the latest models. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
