TAP into the Patch Tokens: Leveraging Vision Foundation Model Features for AI-Generated Image Detection
Recent methods demonstrate that large-scale pretrained models, such as CLIP vision transformers, effectively detect AI-generated images (AIGIs) from unseen generative models when used as feature extractors. Many state-of-the-art methods for AI-generated image detection build upon the original CLIP-ViT to enhance this generalization. Since CLIP's release, numerous vision foundation models (VFMs) have emerged, incorporating architectural improvements and different training paradigms. Despite these advances, their potential for AIGI detection and AI image forensics remains largely unexplored. In this work, we present a comprehensive benchmark across multiple VFM families, covering diverse pr...
| Authors | Ahmed Abdullah, Nikolas Ebert, Oliver Wasenmüller |
|---|---|
| Primary category | cs.CV |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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