TGIF: Text-Guided Inpainting Forgery Dataset

• URL: http://arxiv.org/abs/2407.11566v2
• Date: Fri, 4 Oct 2024 09:39:44 GMT
• Title: TGIF: Text-Guided Inpainting Forgery Dataset
• Abstract summary: Digital image manipulation has become increasingly accessible and realistic with the advent of generative AI technologies. Recent developments allow for text-guided inpainting, making sophisticated image edits possible with minimal effort. This paper introduces the Text-Guided Inpainting Forgery dataset, a comprehensive collection of images designed to support the training and evaluation of image forgery localization and synthetic image detection methods.
• Score: 12.923437316521937
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Digital image manipulation has become increasingly accessible and realistic with the advent of generative AI technologies. Recent developments allow for text-guided inpainting, making sophisticated image edits possible with minimal effort. This poses new challenges for digital media forensics. For example, diffusion model-based approaches could either splice the inpainted region into the original image, or regenerate the entire image. In the latter case, traditional image forgery localization (IFL) methods typically fail. This paper introduces the Text-Guided Inpainting Forgery (TGIF) dataset, a comprehensive collection of images designed to support the training and evaluation of image forgery localization and synthetic image detection (SID) methods. The TGIF dataset includes approximately 75k forged images, originating from popular open-source and commercial methods, namely SD2, SDXL, and Adobe Firefly. We benchmark several state-of-the-art IFL and SID methods on TGIF. Whereas traditional IFL methods can detect spliced images, they fail to detect regenerated inpainted images. Moreover, traditional SID may detect the regenerated inpainted images to be fake, but cannot localize the inpainted area. Finally, both IFL and SID methods fail when exposed to stronger compression, while they are less robust to modern compression algorithms, such as WEBP. In conclusion, this work demonstrates the inefficiency of state-of-the-art detectors on local manipulations performed by modern generative approaches, and aspires to help with the development of more capable IFL and SID methods. The dataset and code can be downloaded at https://github.com/IDLabMedia/tgif-dataset.

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