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Evidence-Guided Detection, Localization and Explanation for Text-Centric Image Forensics
The rapid progress of AIGC has made text-centric image manipulation increasingly accessible, creating new forensic challenges that require not only authenticity detection but also spatial grounding and evidence-based explanation. This paper presents our solution to the GenText-Forensics Challenge at ACM Multimedia 2026. We propose an evidence-guided detector-localizer-reasoner system, where an image-level detector provides a global authenticity prior, a dedicated localizer extracts tampered regions as spatial grounding evidence, and an MLLM-based reasoner generates structured forensic reports
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T04:37:32.000Z
First collected: 2026-09-21T05:51:54.566Z. This is not the publication date.