面向以文本为中心的图像取证的证据引导型检测、定位与解释

中文摘要 AI 辅助

AIGC的快速进展使得以文本为中心的图像篡改愈发容易实现，带来了新的取证挑战，这类挑战不仅需要真实性检测，还需要空间定位和基于证据的解释。本文展示了我们在ACM Multimedia 2026 GenText-Forensics挑战赛中的解决方案。我们提出了一种证据引导型检测器-定位器-推理器系统，其中图像级检测器提供全局真实性先验，专用定位器提取篡改区域作为空间定位证据，基于多模态大语言模型（MLLM）的推理器则生成基于专家取证证据的结构化取证报告。这些模块通过级联证据流连接：检测器控制后续的定位和提示过程，定位器将篡改响应转换为定位框，推理器经训练后将检测器决策和定位证据合成为最终报告。作为我们方法的关键部分，我们引入迭代难度感知挖掘以提升定位质量，并应用报告-掩码一致性后处理来对齐报告定位与预测掩码。在官方隐藏测试集上，我们的系统取得了0.638的最终分数，在挑战赛中排名第二，验证了所提证据引导型系统的有效性。代码可从该httpsURL获取。

英文摘要

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.

发表机构

• Shenzhen University（深圳大学）

机构由 AI 辅助整理，请以论文原文为准。