Summary of Prompt-agnostic Adversarial Perturbation For Customized Diffusion Models, by Cong Wan et al.
Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
by Cong Wan, Yuhang He, Xiang Song, Yihong Gong
First submitted to arxiv on: 20 Aug 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 new paper introduces Prompt-Agnostic Adversarial Perturbation (PAP), a method to defend customized diffusion models against prompt-agnostic attacks. PAP first models the prompt distribution using Laplace Approximation, then generates perturbations by maximizing disturbance expectation based on the modeled distribution. This approach improves defense stability and outperforms existing techniques in face privacy and artistic style protection tasks. The paper demonstrates the effectiveness of PAP through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Customized text-to-image generation has become possible with diffusion models, but this advancement also raises concerns about privacy breaches and unauthorized replication of artworks. Previously, researchers focused on using prompt-specific methods to generate adversarial examples for personal image protection, but these methods have limitations. The new paper presents a solution called Prompt-Agnostic Adversarial Perturbation (PAP) that can defend customized diffusion models against various attacks. |
Keywords
» Artificial intelligence » Image generation » Prompt
