  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 80k forged images, originating from popular
open-source and commercial methods; SD2, SDXL, and Adobe Firefly. Using this
data, we benchmark several state-of-the-art IFL and SID methods. 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 types of methods fail when exposed to stronger compression,
while they are less robust to modern compression algorithms, such as WEBP. As
such, 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 can
be downloaded at https://github.com/IDLabMedia/tgif-dataset.
