信息网络安全 ›› 2016, Vol. 16 ›› Issue (9): 149-153.doi: 10.3969/j.issn.1671-1122.2016.09.030

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基于深度学习的安卓APP视频枪支检测技术研究

雷青(), 荆丽桦, 赵德明, 郑继龙   

  1. 中国科学院信息工程研究所,北京 100093
  • 收稿日期:2016-07-25 出版日期:2016-09-20 发布日期:2020-05-13
  • 作者简介:

    作者简介: 雷青(1991–),女,陕西,硕士研究生,主要研究方向为信息安全;荆丽桦(1993–),女,山西,硕士研究生,主要研究方向为信息安全;赵德明(1986—),男,河北,博士研究生,主要研究方向为计算机视觉;郑继龙(1990—),男,辽宁,助理研究员,硕士,主要研究方向为计算机视觉。

  • 基金资助:
    国家重点研发计划[016YFB0800403]

Research on the Technology of Gun Detection System for Android APP Videos Based on Deep Learning

Qing LEI(), Lihua JING, Deming ZHAO, Jilong ZHENG   

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
  • Received:2016-07-25 Online:2016-09-20 Published:2020-05-13

摘要:

随着移动互联网的飞速发展,基于安卓操作系统的智能终端成为互联网资源最主要流通渠道。由于APP未知应用功能点的不可见性,目前安卓应用商城针对应用程序的功能测试只能采用手工测试,而随着应用数量的快速增长,纯手工测试已经不能满足测试需求。为了进行安卓视频多媒体APP自动监控,净化网络环境,文章提出了一种基于深度学习的安卓APP视频枪支检测方法。该方法突破了从安卓系统底层获取APP视频数据的技术难点,提出了一种基于安卓多媒体框架的视频数据获取方法;针对枪支检测问题,结合深度学习物体检测框架Faster R-CNN,提出了一种局部—整体联合检测方法(Part-Whole-Gun,PWG),显著提高了枪支的检测精度。通过监控并智能分析安卓APP中的视频数据,对含有枪支的敏感视频进行有效过滤,可有效辅助网络环境的净化。

关键词: 安卓, 视频数据获取, 深度学习, 枪支检测

Abstract:

With the rapid development of the Moblile Internet, Android OS has become the most important internet content delivery channels. Unfortunately, different types of videos, which threaten the stability of the society, appear, due to chaotic APP supervision approaches. In this paper, with the objectives of monitoring the Android video applications automatically and thus purifying the network environment, we proposed a gun detection system, which is based on deep learning, for Android APP videos. By overcoming the difficulty of extracting the APP video data from the bottom layer of the Android system, we designed a new video acquiring approach according to the Android multimedia framework. We also developed a part-whole-gun (PWG) detection method based on the Faster R-CNN framework for object detection. Experimental results demonstrate decent performances for practical test images.

Key words: Android, video data capturing, deep learning, gun detection

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