TY - GEN A1 - Yi, Chao A1 - Wu, Shunxiang A1 - Xi, Bin A1 - Ming, Daodong A1 - Zhang, Yisong A1 - Zhou, Zhenwen T1 - Terrorist Video Detection System Based on Faster R-CNN and LightGBM T2 - CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering N2 - Nowadays the mobile phone has become an indispensable tool in the lives of many people. While facilitating people's lives, it also provides criminals with a very important tool for spreading the terrorist video. Traditional manual detection of the terrorist video has the problem of low accuracy and inefficiency. To address the issue, this paper proposes a terrorist video detection system based on Light Gradient Boosting Machine (LightGBM) and Faster Region-based Convolutional Neural Network (Faster R-CNN) for mobile phone forensics system, which is used to quickly detect whether there is a terrorist video in the suspect's mobile phone. The system uses a multi-model method for detection, which includes preliminary detection and deep detection in two stages. Experimental research shows that it can effectively and accurately detect terrorist videos in mobile phones, thereby helping criminal investigation personnel to quickly grasp criminal evidence and provide some clues for the detection of the case. Y1 - 2020 UR - https://dl.acm.org/doi/abs/10.1145/3424978.3425121 SN - 978-1-4503-7772-0 U6 - https://doi.org/10.1145/3424978.3425121 SP - 1 EP - 8 ER -