Biblio

Modern smart surveillance systems can not only record the monitored environment but also identify the targeted objects and detect anomaly activities. These advanced functions are often facilitated by deep neural networks, achieving very high accuracy and large data processing throughput. However, inappropriate design of the neural network may expose such smart systems to the risks of leaking the target being searched or even the adopted learning model itself to attackers. In this talk, we will present the security challenges in the design of smart surveillance systems. We will also discuss some possible solutions that leverage the unique properties of emerging nano-devices, including the incurred design and performance cost and optimization methods for minimizing these overheads.