GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916563334987776 |
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| author | Chen, Yirui Huang, Xudong Zhang, Quan Li, Wei Zhu, Mingjian Yan, Qiangyu Li, Simiao Chen, Hanting Hu, Hailin Yang, Jie Liu, Wei Hu, Jie |
| author_facet | Chen, Yirui Huang, Xudong Zhang, Quan Li, Wei Zhu, Mingjian Yan, Qiangyu Li, Simiao Chen, Hanting Hu, Hailin Yang, Jie Liu, Wei Hu, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16531 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization Chen, Yirui Huang, Xudong Zhang, Quan Li, Wei Zhu, Mingjian Yan, Qiangyu Li, Simiao Chen, Hanting Hu, Hailin Yang, Jie Liu, Wei Hu, Jie Computer Vision and Pattern Recognition 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. |
| title | GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.16531 |