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

TL;DR

ManipShield is a comprehensive framework utilizing a large-scale benchmark and multimodal large language models to improve detection, localization, and explanation of diverse, AI-generated image manipulations, enhancing generalization and interpretability.

Contribution

The paper introduces ManipBench, a large-scale benchmark for diverse image manipulations, and ManipShield, a unified model that achieves state-of-the-art detection, localization, and explanation performance.

Findings

ManipShield outperforms existing methods on ManipBench and public datasets.

ManipShield generalizes well to unseen manipulation models.

ManipBench provides extensive annotated data for interpretability.

Abstract

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.