Summary of Detecting Ai-generated Images Via Clip, by A.g. Moskowitz et al.
Detecting AI-Generated Images via CLIP
by A.G. Moskowitz, T. Gaona, J. Peterson
First submitted to arxiv on: 12 Apr 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 investigates the ability of Contrastive Language-Image Pre-training (CLIP) to differentiate between real images and AI-generated images. CLIP, pre-trained on massive internet-scale datasets, is fine-tuned on real images and AI-generated images from various generative models. The resulting architecture can identify AI-generated images as well or better than models specifically designed for this task. This approach will increase access to AIGI-detecting tools and reduce the negative effects of AI-generated images (AIGI) on society. CLIP’s fine-tuning procedures require no changes to publicly available model repositories, and consume significantly less GPU resources compared to other AIGI detection models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to tell if an image is real or made by a computer. The researchers used a special tool called CLIP that was trained on lots of internet pictures. They made the tool better at identifying fake images by showing it more pictures and making adjustments. The new tool can identify fake images as well or better than other tools specifically designed for this task. This means we can use these tools to detect fake images without having to change the computer’s programming, which will make it easier and faster to identify fake images. |
Keywords
* Artificial intelligence * Fine tuning
