Evidence-Guided Detection, Localization and Explanation for Text-Centric Image Forensics

2026-09-02

AI Summary

本文针对文本为中心的图像鉴伪挑战，提出了一种基于证据引导的检测-定位-解释系统，通过迭代难度感知挖掘和报告-掩码一致性后处理提高鉴伪准确性。

Abstract

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 grounded in this expert forensic evidence. These modules are connected through a cascaded evidence flow: the detector gates the subsequent localization and prompting process, the localizer converts tamper responses into grounding boxes, and the reasoner is trained to synthesize the detector decision and localized evidence into the final report. As a key part of our method, we introduce iterative difficulty-aware mining to improve localization quality and apply report-mask consistency post-processing to align report grounding with predicted masks. On the official hidden test set, our system achieves a final score of 0.638 and ranks second in the challenge, validating the effectiveness of the proposed evidence-guided system. The code is available at https://github.com/peifengLiu42/ACMMM26-evidence-guided-detector-localizer-reasoner-system.

Problem

• text-centric image manipulation
• authenticity detection
• spatial grounding
• evidence-based explanation
• forensic challenges

Innovation

• evidence-guided system
• difficulty-aware mining
• report-mask consistency