Summary of Declip: Decoding Clip Representations For Deepfake Localization, by Stefan Smeu et al.
DeCLIP: Decoding CLIP representations for deepfake localization
by Stefan Smeu, Elisabeta Oneata, Dan Oneata
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 DeCLIP, a novel approach to detect local manipulations in images created by generative models. It leverages large self-supervised models like CLIP and combines them with a convolutional decoder to perform localization and improve generalization capabilities. The authors demonstrate that this approach can generalize better than existing methods, especially on challenging cases like latent diffusion models where the entire image is affected. The paper’s findings suggest that combining local semantic information with global fingerprints can provide more stable generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special kind of detector called DeCLIP to find fake pictures. It takes help from big AI models like CLIP and uses them in a new way. This makes it better at finding fakes than other methods, especially for tricky cases where the whole picture is changed. The authors found that mixing local details with global patterns helps make the detector more reliable. |
Keywords
» Artificial intelligence » Decoder » Generalization » Self supervised
