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 IMDL task unattainable. In this paper, a local
manipulation pipeline is designed, incorporating the powerful SAM, ChatGPT and
generative models. Upon this basis, We propose the GIM dataset, which has the
following advantages: 1) Large scale, including over one million pairs of
AI-manipulated images and real images. 2) Rich Image Content, encompassing a
broad range of image classes 3) Diverse Generative Manipulation, 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 two
benchmark settings to evaluate the generalization capability and comprehensive
performance of baseline 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 Modelling (MWAM)
Module. Extensive experiments on the GIM demonstrate that GIMFormer surpasses
previous state-of-the-art works significantly on two different benchmarks.

我们设计了一个局部操作流程，融合了 SAM、ChatGPT 和生成模型，并提出了 GIM 数据集，该数据集具有大规模、丰富的图像内容和多样的生成操纵，能够更全面地评估 IMDL 方法并扩展其在不同图像上的适用性。此外，我们还提出了一种新颖的 IMDL 框架 GIMFormer，在 GIM 数据集上进行的大量实验证明了其在两个不同基准测试上明显超过了先前的最新工作。