Summary of Fakeinversion: Learning to Detect Images From Unseen Text-to-image Models by Inverting Stable Diffusion, By George Cazenavette et al.
FakeInversion: Learning to Detect Images from Unseen Text-to-Image Models by Inverting Stable Diffusion
by George Cazenavette, Avneesh Sud, Thomas Leung, Ben Usman
First submitted to arxiv on: 12 Jun 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 proposed synthetic image detector utilizes features obtained by inverting a pre-trained Stable Diffusion model to generalize well across unseen generators of high visual fidelity. Trained on lower-fidelity fake images generated via Stable Diffusion, the detector achieves new state-of-the-art performance across multiple training and evaluation setups. The detection approach is based on the idea that the inversion features from the open-source pre-trained Stable Diffusion model can be used to identify synthetic images. This detector is capable of detecting high-fidelity text-to-image models such as DALL-E 3, even when trained only on lower fidelity fake images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to detect synthetic images using features from an open-source Stable Diffusion model. The detector is trained on fake images generated by this model and can recognize high-quality fake images made with other models like DALL-E 3. This method does better than previous detectors in many tests. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
