Summary of Stealthdiffusion: Towards Evading Diffusion Forensic Detection Through Diffusion Model, by Ziyin Zhou et al.
StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model
by Ziyin Zhou, Ke Sun, Zhongxi Chen, Huafeng Kuang, Xiaoshuai Sun, Rongrong Ji
First submitted to arxiv on: 11 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 The paper proposes a framework called StealthDiffusion that generates imperceptible adversarial examples capable of evading state-of-the-art forensic detectors in AI-generated content stealth (AIGC-S). The framework modifies AI-generated images using stable diffusion, comprising two main components: Latent Adversarial Optimization and Control-VAE. These components generate perturbations in the latent space and reduce spectral differences between generated and genuine images without affecting the original diffusion model’s generation process. Experimental results show that StealthDiffusion is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries with frequency spectra similar to genuine images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new method called StealthDiffusion to create fake images that are hard to detect by computers or people. This is important because current methods of making fake images often introduce visible noise and don’t work well in real-world situations. The new method uses stable diffusion, which is a way of generating high-quality images. It has two parts: one that creates small changes in the image’s underlying structure, and another that makes sure the resulting fake image looks similar to real ones. The results show that StealthDiffusion can create very convincing fake images that are hard to detect. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Latent space » Optimization
