在数字法医分析中暴露操纵的照片和视频
Sara Ferreira1, Mário Antunes2,3, Manuel E Correia1,3
1Department of Computer Science, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
Journal of imaging
|July 31, 2024
概括
本研究介绍了一种机器学习方法,使用支持向量机 (SVM) 和离散里埃转换 (DFT) 来检测被改的多媒体内容. 该方法为数字取证提供了高效的深度假冒检测,优于现有的SVM方法.
科学领域:
- 数字法医学数字法医学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 改的多媒体内容,包括深度假冒,在虚假新闻和勒索软件等网络犯罪中很普遍.
- 当前的刑事调查面临着自动区分虚假媒体和真实的媒体的挑战.
- 自动化数字取证工具对于有效检测操纵内容至关重要.
研究的目的:
- 开发和实施基于机器学习的方法来检测被改的数字照片和视频.
- 将这种检测方法集成到Autopsy数字取证平台中.
- 评估该方法在识别深度假冒内容方面的有效性.
主要方法:
- 一个支持矢量机 (SVM) 模型被开发出来,用于将多媒体文件分类为真实或假的.
- 使用适用于图像和视频的离散里埃变换 (DFT) 来提取特征.
- 该方法在Python中实现,并集成到Autopsy模块中.
- 评估涉及大量机密照片和视频数据集,包括用于深度假冒检测的Celeb-DFv1数据集.
主要成果:
- DFT-SVM方法实现了高平均F1分数:照片为99.53%,视频为79.55%,混合内容为89.10%.
- 在文献中,性能超过了现有的基于SVM的方法.
- 虽然卷积神经网络 (CNN) 显示出更高的准确性,但DFT-SVM方法显示出具有竞争力的结果,处理时间大大缩短.
结论:
- 拟议的DFT-SVM方法是一种可行的和有效的工具,用于在数字取证中检测改的多媒体内容.
- 它的整合到Autopsy中有助于自动检测,并帮助刑事调查人员.
- 该方法为识别深度假冒提供了一个实用的解决方案,平衡精度与计算效率.
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