[2603.28613] TGIF2: Extended Text-Guided Inpainting Forgery Dataset & Benchmark

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.28613 (cs) [Submitted on 30 Mar 2026] Title:TGIF2: Extended Text-Guided Inpainting Forgery Dataset & Benchmark Authors:Hannes Mareen, Dimitrios Karageorgiou, Paschalis Giakoumoglou, Peter Lambert, Symeon Papadopoulos, Glenn Van Wallendael View a PDF of the paper titled TGIF2: Extended Text-Guided Inpainting Forgery Dataset & Benchmark, by Hannes Mareen and 5 other authors View PDF HTML (experimental) Abstract:Generative AI has made text-guided inpainting a powerful image editing tool, but at the same time a growing challenge for media forensics. Existing benchmarks, including our text-guided inpainting forgery (TGIF) dataset, show that image forgery localization (IFL) methods can localize manipulations in spliced images but struggle not in fully regenerated (FR) images, while synthetic image detection (SID) methods can detect fully regenerated images but cannot perform localization. With new generative inpainting models emerging and the open problem of localization in FR images remaining, updated datasets and benchmarks are needed. We introduce TGIF2, an extended version of TGIF, that captures recent advances in text-guided inpainting and enables a deeper analysis of forensic robustness. TGIF2 augments the original dataset with edits generated by FLUX.1 models, as well as with random non-semantic masks. Using the TGIF2 dataset, we conduct a forensic evaluation spanning IFL and SID, including fine-tuning ...