LaRE$^2$: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection
Yunpeng Luo, Junlong Du, Ke Yan, Shouhong Ding

TL;DR
LaRE^2 introduces a novel latent reconstruction error feature and an error-guided refinement module to improve the detection of diffusion-generated images, achieving higher accuracy and efficiency than existing methods.
Contribution
The paper presents LaRE, the first latent space reconstruction-error feature for generated image detection, and EGRE, a refinement module that enhances discriminative power.
Findings
Outperforms state-of-the-art methods by up to 12% in accuracy.
Achieves 8 times faster feature extraction.
Effective on multiple image generators in large-scale benchmarks.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Retrieval and Classification Techniques · Medical Image Segmentation Techniques · Generative Adversarial Networks and Image Synthesis
MethodsDiffusion · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
