简短的文本分类方法用于识别儿童性剥削材料
Mhd Wesam Al-Nabki1,2, Eduardo Fidalgo3,4, Enrique Alegre3,4
1Department of Electrical, Systems and Automation Engineering, Universidad de León, León, Spain. wesam.alnabki@unileon.es.
Scientific reports
|September 26, 2023
概括
执法机构 (LEA) 现在可以使用文件名和路径快速识别儿童性剥削材料 (CSEM). 这种新的方法显著加快了数字法医调查的速度,帮助LEA更有效地打击CSEM犯罪.
科学领域:
- 数字法医学数字法医学
- 网络犯罪调查 网络犯罪调查
- 机器学习应用 机器学习应用
背景情况:
- 儿童性剥削材料 (CSEM) 是一项严重的罪行,需要有效的执法机构 (LEA) 调查.
- 对被扣押的数字设备进行手动分析以获取CSEM证据是耗时的,特别是在西班牙的逮捕令限制下.
- 模糊的文件名和命名模式在识别与CSEM相关的文件方面存在挑战.
研究的目的:
- 开发和评估一种方法,通过分析文件名和路径,绕过内容分析来加速CSEM识别.
- 为了应对文件名和路径中扭曲和稀疏文本所带来的挑战.
- 为了对机器学习和卷积神经网络模型进行比较,用于CSEM文件识别.
主要方法:
- 开发了两种方法:一种是结合独立的文件名和路径分类器,另一种是使用对绝对路径的文件名分类器.
- 这两种方法都使用字符n-图形分析.
- 引入了新的二进制和正写特征,以增强文本表示.
主要成果:
- 包括机器学习和卷积神经网络在内的六种分类模型进行了基准测试.
- 拟议的分类器获得了0.988.8的高F1得分.
- 该方法显示了提高CSEM调查效率的巨大潜力.
结论:
- 分析文件名和绝对路径是快速CSEM识别的有效策略.
- 开发的分类器为执法机构 (LEA) 提供了一个有希望的工具,以加快数字法医调查.
- 这种方法可以帮助LEA更有效地打击CSEM的生产和共享.
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