AI生成图像检测器过度依赖全局伪影：来自修复替换的证据

摘要

基于深度学习的现代图像修复技术能够实现逼真的局部图像操控，这对可靠检测提出了严峻挑战。我们发现当前检测器主要依赖作为修复副产物出现的全局伪影，而非局部合成内容。研究表明，这种特性源于VAE重建过程引发的微妙但普遍存在的频谱偏移，该偏移会波及包括未编辑区域在内的整幅图像。为分离该效应，我们提出修复交换操作（INP-X），该操作能在保留所有合成内容的同时，恢复编辑区域外的原始像素。我们构建了包含9万张真实图像、修复图像及交换图像的测试集以评估该现象。在此干预下，包括商业检测器在内的预训练最优模型准确率急剧下降（如从91%降至55%），常趋近随机猜测水平。理论分析表明该现象与VAE信息瓶颈导致的高频衰减相关。研究结果凸显了内容感知检测的必要性：使用本数据集训练的模型相比标准修复数据具有更优的泛化能力和定位精度。数据集与代码已公开于https://github.com/emirhanbilgic/INP-X。

English

Modern deep learning-based inpainting enables realistic local image manipulation, raising critical challenges for reliable detection. However, we observe that current detectors primarily rely on global artifacts that appear as inpainting side effects, rather than on locally synthesized content. We show that this behavior occurs because VAE-based reconstruction induces a subtle but pervasive spectral shift across the entire image, including unedited regions. To isolate this effect, we introduce Inpainting Exchange (INP-X), an operation that restores original pixels outside the edited region while preserving all synthesized content. We create a 90K test dataset including real, inpainted, and exchanged images to evaluate this phenomenon. Under this intervention, pretrained state-of-the-art detectors, including commercial ones, exhibit a dramatic drop in accuracy (e.g., from 91\% to 55\%), frequently approaching chance level. We provide a theoretical analysis linking this behavior to high-frequency attenuation caused by VAE information bottlenecks. Our findings highlight the need for content-aware detection. Indeed, training on our dataset yields better generalization and localization than standard inpainting. Our dataset and code are publicly available at https://github.com/emirhanbilgic/INP-X.

AI生成图像检测器过度依赖全局伪影：来自修复替换的证据

AI-Generated Image Detectors Overrely on Global Artifacts: Evidence from Inpainting Exchange

摘要

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