Local-GAN: An Anomaly Detection Method Based on Local Key Features and GAN for IoT

In Internet of Things (IoT) anomaly detection (AD) tasks, reconstruction-based AD methods are widely concerned due to their simple network structure and ease of training. However, they tend to directly use the original image as the output due to their powerful generalization ability. To overcome this, we propose a novel reconstruction-based anomaly detection method based on local key features and generative adversarial network (named Local-GAN) for IoT. Firstly, Local-GAN utilizes the dual feature encoders to extract local features, and select key features with masks generated by a mask generator. Based on the selected local key features, the decoder avoids reconstructing the anomaly sample/area as it is, which improves the performance. Secondly, the quality-enhanced discriminator is imported into the model to improve the quality of reconstructed images, making it easier to distinguish anomalies. The experiment results on 3 image datasets and 1 real-world industrial image dataset show that our method outperforms reconstruction-based anomaly detection methods in image AD tasks. Furthermore, the experiment results on 13 structured datasets demonstrate the generality of our method.