arXiv 2511.142592026-01-19cs.CV

ManipShield: 一种用于图像篡改检测、定位和解释的统一框架

ManipShield: A Unified Framework for Image Manipulation Detection, Localization and Explanation

Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（上海交通大学图像通信与网络工程研究所）；University of Electronic and Science Technology of China（电子科技大学）；Tianjin University（天津大学）

Zitong Xu, Huiyu Duan, Xiaoyu Wang, Zhaolin Cai, Kaiwei Zhang, Qiang Hu, Jing Liu, Xiongkuo Min, Guangtao Zhai

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AI总结：

ManipShield基于多模态大语言模型，通过对比学习LoRA微调和任务特定解码器，实现图像篡改的统一检测、定位和解释。

摘要：

随着生成模型的快速发展，强大的图像编辑方法现在能够实现多样且高度逼真的图像篡改，远超传统深度伪造技术，给篡改检测带来了新的挑战。现有的图像篡改检测和定位（IMDL）基准存在内容多样性有限、生成模型覆盖范围狭窄和可解释性不足的问题，这阻碍了当前篡改检测方法的泛化和解释能力。为了解决这些限制，我们引入了ManipBench，一个大规模的图像篡改检测和定位基准，专注于AI编辑图像。ManipBench包含超过450,000张由25种最先进的图像编辑模型在12种篡改类别中生成的篡改图像，其中100,000张图像进一步标注了边界框、判断线索和文本解释，以支持可解释的检测。基于ManipBench，我们提出了ManipShield，一个基于多模态大语言模型（MLLM）的统一模型，利用对比学习LoRA微调和任务特定解码器，实现统一的图像篡改检测、定位和解释。在ManipBench和多个公开数据集上的大量实验表明，ManipShield实现了最先进的性能，并表现出对未见过的篡改模型的强大泛化能力。ManipBench和ManipShield将在发表时发布。

英文摘要：

With the rapid advancement of generative models, powerful image editing methods now enable diverse and highly realistic image manipulations that far surpass traditional deepfake techniques, posing new challenges for manipulation detection. Existing image manipulation detection and localization (IMDL) benchmarks suffer from limited content diversity, narrow generative-model coverage, and insufficient interpretability, which hinders the generalization and explanation capabilities of current manipulation detection methods. To address these limitations, we introduce \textbf{ManipBench}, a large-scale benchmark for image manipulation detection and localization focusing on AI-edited images. ManipBench contains over 450K manipulated images produced by 25 state-of-the-art image editing models across 12 manipulation categories, among which 100K images are further annotated with bounding boxes, judgment cues, and textual explanations to support interpretable detection. Building upon ManipBench, we propose \textbf{ManipShield}, an all-in-one model based on a Multimodal Large Language Model (MLLM) that leverages contrastive LoRA fine-tuning and task-specific decoders to achieve unified image manipulation detection, localization, and explanation. Extensive experiments on ManipBench and several public datasets demonstrate that ManipShield achieves state-of-the-art performance and exhibits strong generality to unseen manipulation models. Both ManipBench and ManipShield will be released upon publication.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。