Abstract
The rapid progress of generative AI has enabled highly realistic image manipulations, including inpainting and region-level editing. These approaches preserve most of the original visual context and are increasingly exploited in cybersecurity-relevant threat scenarios. While numerous detectors have been proposed for identifying fully synthetic images, their ability to generalize to localized manipulations remains insufficiently characterized. This work presents a systematic evaluation of state-of-the-art detectors, originally trained for the deepfake detection on fully synthetic images, when applied to a distinct challenge: localized inpainting detection. The study leverages multiple datasets spanning diverse generators, mask sizes, and inpainting techniques. Our experiments show that models trained on a large set of generators exhibit partial transferability to inpainting-based edits and can reliably detect medium- and large-area manipulations or regeneration-style inpainting, outperforming many existing ad hoc detection approaches.
Abstract (translated)
生成式人工智能的迅速进步使得高度逼真的图像操纵成为可能，包括修复绘画（inpainting）和区域级别的编辑。这些方法保留了大部分原始视觉上下文，并且在网络安全相关的威胁场景中被越来越多地利用。虽然已经提出了多种用于识别完全合成图像的检测器，但它们对局部修改的泛化能力尚未充分表征。这项工作系统性地评估了最先进的检测器，在其原本用于深度伪造图像检测的基础上，当应用于不同的挑战时的表现：即局部修复绘画（inpainting）的检测。该研究利用了涵盖多种生成模型、遮罩大小和修复技术的多个数据集。我们的实验表明，基于大量生成模型训练出的模型在面对基于修复绘画的编辑时表现出部分可转移性，并且能够可靠地检测到中等面积和大面积的人为改动或再生风格的修复绘画，优于许多现有的临时检测方法。
URL
https://arxiv.org/abs/2512.16688
