  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.
