Image Anomaly Detection Based on Adaptive Iteration and Feature Extraction in Edge-Cloud IoT

Abstract

The Internet of Things (IoT) has penetrated into various application fields. If the multimedia information obtained by the IoT device is tampered with, the subsequent information processing will be affected, resulting in an incorrect service and even security threat. Therefore, it is very necessary to study multimedia forensics technology for IoT security. In the edge-cloud IoT environment, an image anomaly detection technology for security service is proposed in this paper. First, preprocessing is performed before image anomaly detection. Then, we extracted sparse features from the image to roughly localize the region of anomaly detection. Feature extraction based on the polar cosine transform (PCT) is then performed only on the candidate region of anomaly detection. To further improve the detection accuracy, we use iterative updating. This method makes use of the feature that the edge node is closer to the multimedia source in physical location and migrates the complex computing task of image anomaly detection from the cloud computing center to the edge node. Provide a security service for abnormal data and deploy it to the edge-cloud server to reduce the pressure on the cloud. Overall, preprocessing improves the ability of feature extraction in smooth or small region of anomaly detections, and the iterative strategy enhances the security service. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.