Task-aligned prompting improves zero-shot detection of AI-generated images by Vision-Language Models

• URL: http://arxiv.org/abs/2506.11031v2
• Date: Mon, 16 Jun 2025 01:28:03 GMT
• Title: Task-aligned prompting improves zero-shot detection of AI-generated images by Vision-Language Models
• Authors: Zoher Kachwala, Danishjeet Singh, Danielle Yang, Filippo Menczer,
• Abstract summary: In this work, we investigate the use of pre-trained Vision-Language Models for zero-shot detection of AI-generated images.<n>We show that task-aligned prompting elicits more focused reasoning and significantly improves performance without fine-tuning.<n>Our findings show that task-aligned prompts elicit more focused reasoning and enhance latent capabilities in VLMs.
• Score: 2.005104318774207
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As image generators produce increasingly realistic images, concerns about potential misuse continue to grow. Supervised detection relies on large, curated datasets and struggles to generalize across diverse generators. In this work, we investigate the use of pre-trained Vision-Language Models (VLMs) for zero-shot detection of AI-generated images. While off-the-shelf VLMs exhibit some task-specific reasoning and chain-of-thought prompting offers gains, we show that task-aligned prompting elicits more focused reasoning and significantly improves performance without fine-tuning. Specifically, prefixing the model's response with the phrase "Let's examine the style and the synthesis artifacts" -- a method we call zero-shot-s$^2$ -- boosts Macro F1 scores by 8%-29%. These gains are consistent for two widely used open-source models and across three recent, diverse datasets spanning human faces, objects, and animals with images generated by 16 different models -- demonstrating strong generalization. We further evaluate the approach across three additional model sizes and observe improvements in most dataset-model combinations -- suggesting robustness to model scale. Surprisingly, self-consistency, a behavior previously observed in language reasoning, where aggregating answers from diverse reasoning paths improves performance, also holds in this setting. Even here, zero-shot-s$^2$ scales better than chain-of-thought in most cases -- indicating that it elicits more useful diversity. Our findings show that task-aligned prompts elicit more focused reasoning and enhance latent capabilities in VLMs, like the detection of AI-generated images -- offering a simple, generalizable, and explainable alternative to supervised methods. Our code is publicly available on github: https://github.com/Zoher15/Zero-shot-s2.

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