This research presents a robust adversarial method for anomaly detection in real-world scenarios, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Traditional approaches often falter due to high variance in class-wise accuracy, rendering them ineffective across different anomaly types. Our proposed model addresses these challenges by introducing an innovative flow of information in the training procedure and integrating it as a new discriminator into the framework, thereby optimizing the training dynamics. Furthermore, it employs a supplementary distribution in the input space to steer reconstructions toward the normal data distribution. This adjustment distinctly isolates anomalous instances and enhances detection precision. Also, two unique anomaly scoring mechanisms were developed to augment detection capabilities. Comprehensive evaluations on six varied datasets have confirmed that our model outperforms one-class anomaly detection benchmarks. The implementation is openly accessible to the academic community, available on Github.a.
Keywords: Anomaly detection; anomaly score; cycle consistency; generative adversarial network.