  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.
