Summary of Privacy Protection in Personalized Diffusion Models Via Targeted Cross-attention Adversarial Attack, by Xide Xu et al.
Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack
by Xide Xu, Muhammad Atif Butt, Sandesh Kamath, Bogdan Raducanu
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed paper tackles a pressing concern in personalized text-to-image diffusion models, which have immense potential but also pose significant privacy risks when misused. The authors introduce Concept Protection by Selective Attention Manipulation (CoPSAM), an efficient and novel adversarial attack method targeting the cross-attention layers of these models. By adding imperceptible noise to clean samples during fine-tuning, CoPSAM maximizes the discrepancy between user-specific tokens and class-specific tokens in their corresponding cross-attention maps. The approach outperforms existing methods on a CelebA-HQ face images dataset subset, offering better protection at lower noise levels and safeguarding individual identities from potential misuse. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to keep visual content private by making it harder for bad actors to use personalized text-to-image models. It’s like adding a special kind of noise that makes it hard for hackers to tell the difference between real images and fake ones. The new method, called CoPSAM, is better at protecting privacy than other methods, even when using less noise. This means individual identities are safer from being misused. |
Keywords
» Artificial intelligence » Attention » Cross attention » Diffusion » Fine tuning
