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

TL;DR

This paper introduces Prefill-Guided Thinking (PGT), a method that enhances zero-shot detection of AI-generated images by guiding pre-trained vision-language models with specific prompts, significantly improving their accuracy across diverse datasets.

Contribution

The paper proposes PGT, a novel prompting technique that improves the zero-shot detection of AI-generated images using pre-trained VLMs, addressing generalization issues of traditional supervised methods.

Findings

Prefill-Guided Thinking improves detection scores by up to 24%.

Prefilling responses helps mitigate overconfidence in models.

The method enhances zero-shot detection across diverse image types.

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.