GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization

Yirui Chen; Xudong Huang; Quan Zhang; Wei Li; Mingjian Zhu; Qiangyu Yan; Simiao Li; Hanting Chen; Hailin Hu; Jie Yang; Wei Liu; Jie Hu

arXiv · 2406.16531

GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization

Yirui Chen·Xudong Huang·Quan Zhang·Wei Li·Mingjian Zhu·Qiangyu Yan·Simiao Li·Hanting Chen·Hailin Hu·Jie Yang·Wei Liu·Jie Hu

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Abstract

The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia data and driving the research of image manipulation detection and location (IMDL). However, the lack of a large-scale data foundation makes the IMDL task unattainable. In this paper, we build a local manipulation data generation pipeline that integrates the powerful capabilities of SAM, LLM, and generative models. Upon this basis, we propose the GIM dataset, which has the following advantages: 1) Large scale, GIM includes over one million pairs of AI-manipulated images and real images. 2) Rich image content, GIM encompasses a broad range of image classes. 3) Diverse generative manipulation, the images are manipulated images with state-of-the-art generators and various manipulation tasks. The aforementioned advantages allow for a more comprehensive evaluation of IMDL methods, extending their applicability to diverse images. We introduce the GIM benchmark with two settings to evaluate existing IMDL methods. In addition, we propose a novel IMDL framework, termed GIMFormer, which consists of a ShadowTracer, Frequency-Spatial block (FSB), and a Multi-Window Anomalous Modeling (MWAM) module. Extensive experiments on the GIM demonstrate that GIMFormer surpasses the previous state-of-the-art approach on two different benchmarks.

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BibTeX RIS

Yirui Chen, Xudong Huang, Quan Zhang, Wei Li, Mingjian Zhu, Qiangyu Yan, Simiao Li, Hanting Chen, Hailin Hu, Jie Yang, Wei Liu, Jie Hu. 2025-01-13. GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization. https://arxiv.org/abs/2406.16531

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