Summary of Exploiting Watermark-based Defense Mechanisms in Text-to-image Diffusion Models For Unauthorized Data Usage, by Soumil Datta et al.
Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage
by Soumil Datta, Shih-Chieh Dai, Leo Yu, Guanhong Tao
First submitted to arxiv on: 22 Nov 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 This paper investigates the effectiveness of watermark-based protection methods for text-to-image diffusion models, particularly Stable Diffusion. The authors highlight concerns over unauthorized data use in training these models, which may lead to intellectual property infringement or privacy violations. To address this issue, they propose a novel approach called RATTAN that leverages the diffusion process to generate controlled images on protected inputs, preserving high-level features while ignoring low-level details used by watermarks. The authors demonstrate the robustness of their method against existing state-of-the-art protections using three datasets and 140 text-to-image diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to keep sensitive information safe when training computers to generate images from text descriptions. Some computer programs, like Stable Diffusion, can create very realistic pictures. However, these programs might use private or copyrighted data without permission. To solve this problem, the researchers developed a new method called RATTAN that hides secret markers in the generated images. They tested their approach on several datasets and found that it works well against existing security methods. |
Keywords
» Artificial intelligence » Diffusion
