Summary of Watermark-embedded Adversarial Examples For Copyright Protection Against Diffusion Models, by Peifei Zhu et al.
Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models
by Peifei Zhu, Tsubasa Takahashi, Hirokatsu Kataoka
First submitted to arxiv on: 15 Apr 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 proposed framework embeds personal watermarks in the generation of adversarial examples to prevent diffusion models (DMs) from imitating unauthorized images and raise copyright issues. The generator is based on conditional adversarial networks and designed with three losses to generate subtle yet effective perturbations that attack DMs. This approach can be trained quickly, requiring only 5-10 samples in 2-3 minutes, and generates adversarial examples at a rate of 0.2 seconds per image. The method is tested in various conditional image-generation scenarios, outperforming existing methods by adding visible watermarks to generated images, making it a straightforward way to indicate copyright violations. Furthermore, the proposed adversarial examples exhibit good transferability across unknown generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to prevent diffusion models from copying unauthorized images and violating copyrights. It does this by hiding special marks, or “watermarks,” in the generated images that can’t be easily removed. The team developed a machine learning model that can create these watermarked images quickly and efficiently. They tested their approach on various image-generation tasks and found it to be effective at stopping unauthorized copying. This could help protect artists and creators from having their work copied without permission. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Machine learning » Transferability
