Measuring Distortion Strength with Dewarping Diffusion Models in Anomaly Detection

Surface anomaly detection is a task to localize abnormal regions in a given image, typically used for product inspection. A representative approach is the reconstruction-based method, which detects defects using reconstruction errors computed by generative models, such as diffusion models trained exclusively on normal images. In reality, however, for products largely comprised of metal or resin, it is common to identify abnormalities based on local physical distortion levels; i.e., regions exceeding a predefined tolerance are regarded as defective. Reconstruction error-based approaches cannot directly estimate this metric. To address this issue, we propose DiffuDewarp, a novel method that directly estimates local distortions. Our approach defines a pseudo-deformation defect generation process as a new diffusion process based on localized warping. Experiments on the MVTec dataset demonstrate that our method outperforms state-of-the-art techniques in categories where local deformations are the primary cause of defects. Code is released at https://github.com/UCHIDA-AKIRA018/DiffuDewarp.