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Terrorist Video Detection System Based on Faster R-CNN and LightGBM

  • 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.

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Author: Chao Yi, Shunxiang Wu, Bin Xi, Daodong Ming, Yisong Zhang, Zhenwen Zhou
URL:https://dl.acm.org/doi/abs/10.1145/3424978.3425121
DOI:https://doi.org/10.1145/3424978.3425121
ISBN:978-1-4503-7772-0
Title of the source (English):CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2020
Contributing Corporation:ACM
First Page:1
Last Page:8
Article number:136
Way of publication:Open Access
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG IT-Sicherheit
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