检测局部深度伪造：合成图像检测器如何处理修补？
Detecting Localized Deepfakes: How Well Do Synthetic Image Detectors Handle Inpainting?
AI总结：
本文评估了现有合成图像检测器在局部修补检测中的表现，发现训练于大规模生成器数据的模型能有效检测中等和大区域的修补，优于现有方法。
摘要：
生成式人工智能的快速发展使高度逼真的图像篡改技术得以实现，包括修补和区域级编辑。这些方法保留了大部分原始视觉上下文，并越来越多地被用于网络安全相关的威胁场景中。尽管已经提出了许多用于识别完全合成图像的检测器，但它们在处理局部篡改方面的能力仍不够明确。本文系统评估了最先进的检测器在处理一个不同挑战：局部修补检测时的表现。研究利用了涵盖多种生成器、遮罩大小和修补技术的多个数据集。我们的实验表明，训练在大量生成器数据集上的模型在修补类编辑上表现出部分迁移能力，并能可靠地检测中等和大区域的篡改或再生式修补，优于许多现有的即兴检测方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
