Summary of Tgif: Text-guided Inpainting Forgery Dataset, by Hannes Mareen et al.
TGIF: Text-Guided Inpainting Forgery Dataset
by Hannes Mareen, Dimitrios Karageorgiou, Glenn Van Wallendael, Peter Lambert, Symeon Papadopoulos
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of detecting digital image forgery in images edited using generative AI technologies. Specifically, it focuses on text-guided inpainting, which allows for sophisticated edits with minimal effort. The authors introduce a new dataset, TGIF, comprising approximately 75k forged images generated by popular open-source and commercial methods, including SD2, SDXL, and Adobe Firefly. They benchmark several state-of-the-art image forgery localization (IFL) and synthetic image detection (SID) methods on this dataset. The results show that traditional IFL methods can detect spliced images but fail to detect regenerated inpainted images, while SID methods may detect the fake images but cannot localize the inpainted area. Moreover, both IFL and SID methods are less robust when exposed to stronger compression algorithms like WEBP. This work highlights the inefficiency of current detectors in detecting local manipulations performed by modern generative approaches, aiming to spur the development of more capable IFL and SID methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure digital images aren’t fake or edited in ways that are hard to detect. With the help of computers, people can easily edit images now. This makes it harder for experts to figure out if an image has been changed. The authors created a big collection of edited images to test different methods for detecting these changes. They found that current methods can spot some fake images but struggle with others. They also showed that these methods are not very good at handling certain types of compression, like the kind used on websites. This research is important because it helps us understand how to improve our ability to detect when digital images have been edited or faked. |
