The evolution of Diffusion Models has dramatically improved image generation quality, making it increasingly difficult to differentiate between real and generated images. This development, while impressive, also raises significant privacy and security concerns. In response to this, we propose a novel Latent REconstruction error guided feature REfinement method (LaRE^2) for detecting the diffusion-generated images. We come up with the Latent Reconstruction Error (LaRE), the first reconstruction-error based feature in the latent space for generated image detection. LaRE surpasses existing methods in terms of feature extraction efficiency while preserving crucial cues required to differentiate between the real and the fake. To exploit LaRE, we propose an Error-Guided feature REfinement module (EGRE), which can refine the image feature guided by LaRE to enhance the discriminativeness of the feature. Our EGRE utilizes an align-then-refine mechanism, which effectively refines the image feature for generated-image detection from both spatial and channel perspectives. Extensive experiments on the large-scale GenImage benchmark demonstrate the superiority of our LaRE^2, which surpasses the best SoTA method by up to 11.9%/12.1% average ACC/AP across 8 different image generators. LaRE also surpasses existing methods in terms of feature extraction cost, delivering an impressive speed enhancement of 8 times.

本文提出了一种基于潜在重建误差引导特征细化的新方法LaRE^2，用于检测扩散生成的图像，并通过Error-Guided feature REfinement模块（EGRE）来增强特征的区分性。实验结果表明，在8种不同的图像生成器上，LaRE^2的平均准确率（ACC）和平均精度（AP）相较于现有最佳方法提高了11.9%/12.1%。LaRE还在特征提取成本方面超过了现有方法，提供了8倍的速度增强。

基于潜在重建误差的扩散生成图像检测方法 LaRE^2