Summary of How to Trace Latent Generative Model Generated Images Without Artificial Watermark?, by Zhenting Wang et al.
How to Trace Latent Generative Model Generated Images without Artificial Watermark?
by Zhenting Wang, Vikash Sehwag, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes a method called LatentTracer to trace the origin of images generated by latent generative models, such as Stable Diffusion. The authors aim to analyze whether it is possible to effectively and efficiently determine if an image was generated by a specific model without requiring extra steps during training or generation. They design a latent inversion-based approach that leverages gradient-based latent inversion and encoder-based initialization. Experiments on state-of-the-art models, including Stable Diffusion, demonstrate the method’s high accuracy and efficiency in distinguishing between images generated by the inspected model and other images. The findings suggest that today’s latent generative models may naturally watermark their generated images with the decoder used in the source models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding out where an image came from if it was made using a special type of computer program called a latent generative model. These programs can create realistic-looking pictures, but there are concerns that they could be used to spread misinformation or fake news. The researchers developed a new way to figure out what kind of program created an image without having to modify the program itself. They tested their method on different types of images and found that it was very good at identifying which program made which picture. |
Keywords
» Artificial intelligence » Decoder » Diffusion » Encoder » Generative model
