Summary of Freezeasguard: Mitigating Illegal Adaptation Of Diffusion Models Via Selective Tensor Freezing, by Kai Huang et al.
FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing
by Kai Huang, Haoming Wang, Wei Gao
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes FreezeAsGuard, a technique to prevent the illegal adaptation of diffusion models for text-to-image generation. While existing methods focused on detecting illegally generated contents, they failed to prevent or mitigate such adaptations. FreezeAsGuard selectively freezes tensors in pre-trained models that are critical to illegal adaptations, minimizing the impact on legal uses while providing stronger protection against illegal model adaptations (37% improvement over baselines). The technique is tested in multiple domains and shows promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you can create pictures of anything just by typing words! This technology is called diffusion models. But some people misuse this power to make fake portraits, copy famous artworks, or generate inappropriate content. To stop this from happening, researchers developed a new method called FreezeAsGuard. It’s like putting a lock on the model that makes it hard for bad people to adapt it for illegal uses, while still allowing good people to use it for fun and creative purposes. |
Keywords
» Artificial intelligence » Diffusion » Image generation
