Summary of Detecting Autoencoder Is Enough to Catch Ldm Generated Images, by Dmitry Vesnin et al.
Detecting AutoEncoder is Enough to Catch LDM Generated Images
by Dmitry Vesnin, Dmitry Levshun, Andrey Chechulin
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); 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 proposed method detects images generated by Latent Diffusion Models (LDM) by identifying artifacts introduced by their autoencoders. Without directly training on synthesized data, the detector distinguishes between real and reconstructed images. This reduces computational costs and enhances generalization ability. Experimental results demonstrate high detection accuracy with minimal false positives, making this approach a promising tool for combating fake images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to detect images made by Latent Diffusion Models (LDM). Usually, detecting these generated images is hard. The key idea is to look at the special things that LDM autoencoders do to the images they make. By training a detector to tell real images apart from ones made by LDM autoencoders, this method can detect fake images without needing to train on those images themselves. This makes it fast and good at detecting fake images. |
Keywords
» Artificial intelligence » Diffusion » Generalization
