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One pivot challenge for image anomaly (AD) detection is to learndiscriminative information only from normal class training images. Most imagereconstruction based AD methods rely on the discriminative capability ofreconstruction error. This is heuristic as image reconstruction is unsupervisedwithout incorporating normal-class-specific information. In this paper, wepropose an AD method called dual deep reconstruction networks based imagedecomposition (DDR-ID). The networks are trained by jointly optimizing forthree losses: the one-class loss, the latent space constrain loss and thereconstruction loss. After training, DDR-ID can decompose an unseen image intoits normal class and the residual components, respectively. Two anomaly scoresare calculated to quantify the anomalous degree of the image in either normalclass latent space or reconstruction image space. Thereby, anomaly detectioncan be performed via thresholding the anomaly score. The experimentsdemonstrate that DDR-ID outperforms multiple related benchmarking methods inimage anomaly detection using MNIST, CIFAR-10 and Endosome datasets andadversarial attack detection using GTSRB dataset.

DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection