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 IFL methods on FR images and generative super-resolution attacks. Our experiments show that both IFL and SID methods degrade on FLUX.1 manipulations, highlighting limited generalization. Additionally, while fine-tuning improves localization on FR images, evaluation with random non-semantic masks reveals object bias. Furthermore, generative super-resolution significantly weakens forensic traces, demonstrating that common image enhancement operations can undermine current forensic pipelines. In summary, TGIF2 provides an updated dataset and benchmark, which enables new insights into the challenges posed by modern inpainting and AI-based image enhancements. TGIF2 is available at https://github.com/IDLabMedia/tgif-dataset.

翻译：生成式AI已将文本引导修复发展为强大的图像编辑工具，但同时也为媒体取证带来了日益严峻的挑战。现有基准测试（包括我们的文本引导修复伪造（TGIF）数据集）表明：图像伪造定位（IFL）方法能够检测拼接图像中的篡改区域，但在完全生成（FR）图像中表现不佳；而合成图像检测（SID）方法虽能识别完全生成图像，却无法实现区域定位。随着新型生成式修复模型的涌现以及FR图像定位难题的持续存在，亟需更新的数据集与基准测试。本文提出TGIF2——TGIF的扩展版本，该数据集捕捉了文本引导修复领域的最新进展，并支持对取证鲁棒性的深度分析。TGIF2通过FLUX.1模型生成的编辑结果及随机非语义掩码对原始数据集进行扩充。利用TGIF2数据集，我们开展了涵盖IFL与SID的取证评估，包括在FR图像上微调IFL方法以及生成式超分辨率攻击实验。实验结果表明：IFL和SID方法在FLUX.1生成的篡改图像上性能均显著下降，凸显其泛化能力不足；同时，虽然微调提升了FR图像上的定位效果，但随机非语义掩码的评估揭示了对象偏见。此外，生成式超分辨率操作会显著削弱取证痕迹，表明常见图像增强手段可破坏现有取证流程。综上所述，TGIF2作为更新版数据集与基准测试，为理解现代修复技术与AI图像增强带来的新挑战提供了重要依据。TGIF2数据集可通过https://github.com/IDLabMedia/tgif-dataset 获取。