CLIP Embeddings for AI-Generated Image Detection: A Few-Shot Study with Lightweight Classifier

• URL: http://arxiv.org/abs/2505.10664v1
• Date: Thu, 15 May 2025 19:14:39 GMT
• Title: CLIP Embeddings for AI-Generated Image Detection: A Few-Shot Study with Lightweight Classifier
• Authors: Ziyang Ou,
• Abstract summary: This work investigates whether CLIP embeddings inherently contain indicative information of AI generation.<n>Experiments on the public CIFAKE benchmark show the performance reaches 95% accuracy without language reasoning.<n>Some specific image types, such as wide-angle photographs and oil paintings, pose significant challenges to classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Verifying the authenticity of AI-generated images presents a growing challenge on social media platforms these days. While vision-language models (VLMs) like CLIP outdo in multimodal representation, their capacity for AI-generated image classification is underexplored due to the absence of such labels during the pre-training process. This work investigates whether CLIP embeddings inherently contain information indicative of AI generation. A proposed pipeline extracts visual embeddings using a frozen CLIP model, feeds its embeddings to lightweight networks, and fine-tunes only the final classifier. Experiments on the public CIFAKE benchmark show the performance reaches 95% accuracy without language reasoning. Few-shot adaptation to curated custom with 20% of the data results in performance to 85%. A closed-source baseline (Gemini-2.0) has the best zero-shot accuracy yet fails on specific styles. Notably, some specific image types, such as wide-angle photographs and oil paintings, pose significant challenges to classification. These results indicate previously unexplored difficulties in classifying certain types of AI-generated images, revealing new and more specific questions in this domain that are worth further investigation.

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