一个强大的方法,多模式深度假冒检测
Davide Salvi1, Honggu Liu2, Sara Mandelli1
1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.
Journal of imaging
|June 27, 2023
概括
检测深度假视频至关重要. 这项研究引入了一种新的多式联络方法,使用时间感知神经网络来识别音频和视觉数据之间的不一致,即使在单独的数据集上训练.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数字法医学数字法医学
背景情况:
- 深度学习能够实现现实的合成媒体 (deepfakes),构成社会威胁.
- 目前的深度假冒检测方法在多式联运一致性和时间准确性方面扎.
- 从虚假媒体区分真实的越来越重要.
研究的目的:
- 提出一种新的方法来检测深度假冒视频序列,使用数据多模式.
- 为了提高检测,利用视听数据之间和视听数据内部的不一致性.
- 开发一个强大的深度假冒探测器,训练在不连接的单模数据集.
主要方法:
- 从视频序列中提取时间意识的视听特征.
- 使用时间感知神经网络进行分析,以确定模式间的不一致性.
- 针对单独的视觉和音频深度假冒数据集进行培训,对多式联络深度假冒进行测试.
主要成果:
- 多式联网方法显著优于单式联网检测.
- 拟议的方法证明了在未见的多式联运深层假冒上具有稳定性.
- 研究和比较不同的数据融合技术,以获得最佳性能.
结论:
- 利用视听不一致性是深度假冒检测的有效方法.
- 拟议的培训战略克服了稀缺的多式联运深度假冒数据集的局限性.
- 这种方法为识别复杂的深度假视频提供了强大的解决方案.
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