Application of Computational Complexity Theory to Pornographic Detection in Live Based on Frame Sampling
Hanxi Li, Xiangkun Guo, Hongliang Wang, Bihui Yu, Peipei Zhao · 2020
In order to prevent people from troubling by the sensitive information in the Internet, it is urgently compulsory to fortify the supervision upon Internet culture. Our works mainly focus on the online-live and internet short videos realtime invigilation. In this paper we leverage a novel deep learning (DL) model which is constructed by using Late Fusion method, also combining frame sampling to detect the sensitive information in stream mode. Plus, we use computational complexity theory to optimize the classification accuracy of specific samples. Our experiment shows that it is feasible to apply the theory into the DL model. After the optimization, the result has a certain improvement in classification accuracy compared with the naïve form.