Deep Support Vector Data Description with Edge Outlier Exposure for Image-Based Anomaly Detection

• Guowei Yang,
• Min Gao,
• Minghua Wan,

Vol. 19, No. 12, December 31, 2025
10.3837/tiis.2025.12.005, Download Paper (Free):

• Deep Neural Networks
• Anomaly Detection
• outlier exposure
• feature representation

Abstract

In image-based anomaly detection scenarios, current approaches such as deep neural network-based methods (DSVDD), self-supervised learning-based methods (DASVDD), and complete learning-based methods (CDSVDD), are primarily driven by positive (normal) samples. This reliance poses a risk wherein abnormal sample features may fall within the feature space of normal samples, especially when there is significant overlap between the distributions of normal and abnormal samples. To address the aforementioned issues, by absorbing the essence of outlier exposure open-set recognition, we propose an innovative method for anomaly detection called DSVDD-EOE. Our approach aims to minimize the enclosed hypersphere containing the feature region of positive samples while controlling the optimized edge outlier exposure set feature outside of this hypersphere. Unlike existing methods, the proposed method considers the center of the hypersphere as a learnable parameter that can be adjusted according to an evolved deep feature representation. In addition, we construct and optimize the outlier exposure set to participate in anomaly detection modeling, which significantly reduces the likelihood of mapping abnormal sample features into the domain of normal sample features. Experimental results demonstrate that the proposed method achieved an average area under the curve (AUC) value of 90.8% on the CIFAR-10 image benchmark dataset, which is 19.5% higher than that achieved by current state-of-the-art anomaly detection methods. On the FMNIST dataset, the proposed method achieved an impressive 95.8% average AUC value, and on the MNIST dataset, the proposed method achieved an average AUC value of 98.9%, both exceeding the performance of prior techniques. In addition, the proposed method demonstrated superior performance on the more demanding Tiny ImageNet dataset.

Statistics

Cite this article