Summary of Cgi-dm: Digital Copyright Authentication For Diffusion Models Via Contrasting Gradient Inversion, by Xiaoyu Wu et al.
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion
by Xiaoyu Wu, Yang Hua, Chumeng Liang, Jiaru Zhang, Hao Wang, Tao Song, Haibing Guan
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY); 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 presents a novel method called Contrasting Gradient Inversion for Diffusion Models (CGI-DM) that addresses concerns about potential copyright violations in few-shot image generation. The approach removes partial information from an image and recovers missing details by exploiting conceptual differences between pretrained and fine-tuned models. This is achieved through KL divergence between latent variables, maximized using Monte Carlo sampling and Projected Gradient Descent (PGD). The similarity between original and recovered images serves as a strong indicator of potential infringements. The paper demonstrates the high accuracy of CGI-DM in digital copyright authentication on WikiArt and Dreambooth datasets, outperforming alternative validation techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CGI-DM is a new way to check if an image has been copied without permission. This problem happens when someone uses a pre-trained computer model to generate an image that looks like another image they saw before. The concern is that the generated image might be a copy of the original, and that’s not allowed. CGI-DM works by taking an image, removing some parts, and then trying to recover those missing details. It does this by finding the differences between how the model was trained originally and how it was fine-tuned for the specific task. This difference is like a fingerprint, and if it matches the original image, it’s likely that the generated image was copied without permission. |
Keywords
» Artificial intelligence » Diffusion » Few shot » Gradient descent » Image generation
