Zoom-In to Sort AI-Generated Images Out

• URL: http://arxiv.org/abs/2510.04225v1
• Date: Sun, 05 Oct 2025 14:29:01 GMT
• Title: Zoom-In to Sort AI-Generated Images Out
• Authors: Yikun Ji, Yan Hong, Bowen Deng, jun lan, Huijia Zhu, Weiqiang Wang, Liqing Zhang, Jianfu Zhang,
• Abstract summary: We propose ZoomIn, a two-stage forensic framework that improves both accuracy and interpretability.<n>To support training, we introduce MagniFake, a dataset of 20,000 real and high-quality synthetic images annotated with bounding boxes and forensic explanations.<n>Our method achieves 96.39% accuracy with robust generalization, while providing human-understandable explanations grounded in visual evidence.
• Score: 34.49867697753459
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising critical concerns for digital integrity. Vision-language models (VLMs) offer interpretability through explanations but often fail to detect subtle artifacts in high-quality synthetic images. We propose ZoomIn, a two-stage forensic framework that improves both accuracy and interpretability. Mimicking human visual inspection, ZoomIn first scans an image to locate suspicious regions and then performs a focused analysis on these zoomed-in areas to deliver a grounded verdict. To support training, we introduce MagniFake, a dataset of 20,000 real and high-quality synthetic images annotated with bounding boxes and forensic explanations, generated through an automated VLM-based pipeline. Our method achieves 96.39% accuracy with robust generalization, while providing human-understandable explanations grounded in visual evidence.

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