Diffusion models have recently achieved remarkable photorealism, making it increasingly difficult to distinguish real images from generated ones, raising significant privacy and security concerns. In response, we present a key finding: structural edits enhance the reconstruction of real images while degrading that of generated images, creating a distinctive edit-induced reconstruction error shift. This asymmetric shift enhances the separability between real and generated images. Building on this insight, we propose EIRES, a training-free method that leverages structural edits to reveal inherent differences between real and generated images. To explain the discriminative power of this shift, we derive the reconstruction error lower bound under edit perturbations. Since EIRES requires no training, thresholding depends solely on the natural separability of the signal, where a larger margin yields more reliable detection. Extensive experiments show that EIRES is effective across diverse generative models and remains robust on the unbiased subset, even under post-processing operations.

翻译：扩散模型近期实现了显著的逼真度提升，使得真实图像与生成图像愈发难以区分，引发了严重的隐私与安全担忧。对此，我们提出一项关键发现：结构化编辑能够增强真实图像的重建质量，同时劣化生成图像的重建效果，从而产生独特的编辑诱导重建误差偏移。这种非对称偏移增强了真实图像与生成图像之间的可分离性。基于这一洞察，我们提出EIRES——一种利用结构化编辑揭示真实与生成图像内在差异的无训练检测方法。为解释该偏移的判别能力，我们推导了编辑扰动下重建误差的理论下界。由于EIRES无需训练，其阈值设定完全依赖于信号的自然可分离性，更大的分离裕度意味着更可靠的检测性能。大量实验表明，EIRES对多种生成模型均具有检测效力，并在无偏子集上保持鲁棒性，即使面对后处理操作时亦然。