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arXiv · 2506.11031

Prefill-Guided Thinking for zero-shot detection of AI-generated images

Zoher Kachwala·Danishjeet Singh·Danielle Yang·Filippo Menczer
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Abstract

Traditional supervised methods for detecting AI-generated images depend on large, curated datasets for training and fail to generalize to novel, out-of-domain image generators. As an alternative, we explore pre-trained Vision-Language Models (VLMs) for zero-shot detection of AI-generated images. We evaluate VLM performance on three diverse benchmarks encompassing synthetic images of human faces, objects, and animals produced by 16 different state-of-the-art image generators. While off-the-shelf VLMs perform poorly on these datasets, we find that prefilling responses effectively guides their reasoning -- a method we call Prefill-Guided Thinking (PGT). In particular, prefilling a VLM response with the phrase "Let's examine the style and the synthesis artifacts" improves the Macro F1 scores of three widely used open-source VLMs by up to 24%. We analyze this improvement in detection by tracking answer confidence during response generation. For some models, prefills counteract early overconfidence -- akin to mitigating the Dunning-Kruger effect -- leading to better detection performance.

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Zoher Kachwala, Danishjeet Singh, Danielle Yang, Filippo Menczer. 2025-05-20. Prefill-Guided Thinking for zero-shot detection of AI-generated images. https://arxiv.org/abs/2506.11031

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