Summary of Contrasting Deepfakes Diffusion Via Contrastive Learning and Global-local Similarities, by Lorenzo Baraldi et al.
Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities
by Lorenzo Baraldi, Federico Cocchi, Marcella Cornia, Lorenzo Baraldi, Alessandro Nicolosi, Rita Cucchiara
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 This paper proposes a novel approach to detecting deepfakes, which are images generated by advanced AI methods. The current state-of-the-art method, CLIP, is not specifically designed for deepfake detection and neglects local image features. To address this gap, the authors propose CoDE (Contrastive Deepfake Embeddings), an embedding space tailored for deepfake detection. CoDE is trained using contrastive learning to enforce global-local similarities. The model is evaluated on a comprehensive dataset of 9.2 million images generated by four different diffusion models, achieving state-of-the-art accuracy and excellent generalization capabilities. The authors also provide public access to their source code, trained models, and collected dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out whether an image was made by a machine or not. Right now, it’s hard to tell because the current best method isn’t designed specifically for this task. The researchers come up with a new way called CoDE that’s good at spotting fake images. They train CoDE using special learning techniques and test it on a huge dataset of 9.2 million images made by different machines. CoDE does really well and can even recognize fake images from machines it hasn’t seen before. |
Keywords
» Artificial intelligence » Embedding space » Generalization
