测试主流假图检测模型对局部修复图像的识别能力
Detecting Localized Deepfakes: How Well Do Synthetic Image Detectors Handle Inpainting?
- 用多种生成器和修复技术测试检测模型泛化性
- 大范围修复或重绘类操作能被较好识别，小范围效果差
- 适合关注图像安全与内容可信度的研究者参考
生成式AI的快速发展使得图像局部修复和区域编辑变得高度逼真，这类操作保留了大部分原始视觉上下文，在网络安全威胁中日益常见。尽管已有大量针对完全合成图像的假图检测方法，但其对局部篡改的泛化能力仍缺乏系统评估。本文系统评估了原本用于全图合成假图检测的先进模型在局部修复检测任务上的表现。实验基于多个数据集，涵盖不同生成器、掩码尺寸和修复技术。结果表明，训练于大规模生成器的数据集的模型对局部修复具有部分迁移能力，能可靠识别中大尺度篡改或重绘类修复，优于许多现有特定设计的检测方法。
原文摘要 · Abstract (English)
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.
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