Self-Supervised Anomaly Detection and a New Benchmark for X-Ray Cargo Images

Abstract

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Robust detection of illicit items using X-ray inspection methods has gained increasing importance in recent years due to the large volume of cargo crossing borders. Viewing this as an anomaly detection problem in which contraband items are anomalous relative to expected cargo, we propose a selfsupervised learning framework consisting of an encoderdecoder-classifier model, a multi-component loss function, and training strategy to extract discriminative features tailored for detection of the presence or absence of anomalies in X-ray cargo images. Our framework addresses the challenges posed by limited labeled data and provides a model with the ability to detect anomalies. Additionally, we created a dataset encompassing diverse cargo scenarios with and without anomalies providing a rigorous evaluation environment for anomaly detection problems. We explore the performance of our framework on our dataset and two other datasets, demonstrating robustness even in testing with complex novel anomalies significantly different from those encountered during training.