SecureAD: A secure video anomaly detection framework on convolutional neural network in edge computing environment

• Main Authors: CHENG, Hang, LIU, Ximeng, WANG, Huaxiong, FANG, Yan, WANG, Meiqing, ZHAO, Xiaopeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Privacy-preserving; anomaly detection; Bloom filter; CNN; secret sharing; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/5932; https://ink.library.smu.edu.sg/context/sis_research/article/6935/viewcontent/SecureAD_av_2020.pdf
• Institution: Singapore Management University

Anomaly detection offers a powerful approach to identifying unusual activities and uncommon behaviors in real-world video scenes. At present, convolutional neural networks (CNN) have been widely used to tackle anomalous events detection, which mainly rely on its stronger ability of feature represent...