完全不受监督的Deepfake视频检测通过增强的对比学习.
IEEE transactions on pattern analysis and machine intelligence
|January 22, 2024
概括
这项研究引入了一种新的无监督深度假冒检测器,不需要标记数据. 该方法使用伪标签生成器和对比学习来有效识别深度假视频,即使有毒或不足的训练数据.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 深度假冒视频对公众信任和社会安全构成重大威胁.
- 现有的深度假冒检测器通常依赖于监督学习,需要大型标记数据集.
- 监督的方法容易受到训练数据不足或恶意中毒的影响.
研究的目的:
- 开发一种完全不受监督的深度假冒检测方法.
- 解决监督深度假冒检测在数据可用性和完整性方面的局限性.
- 为了创建一个可靠的深度假冒检测器,在没有任何样品标签的任何事先知识的情况下运行.
主要方法:
- 一个新的伪标签生成器,利用手工制作的功能来标签训练样本.
- 一个增强的对比学习器,以代地提取和完善歧视性特征.
- 基于框架间相关性的二进制分类,用于最终的深度假冒检测.
主要成果:
- 拟议的无监督探测器在多个基准数据集 (FF++,Celeb-DF,DFD,DFDC,UADFV) 中显示出有效性.
- 达到与监督方法相提并论的性能,并且优于现有的无监督方法.
- 在处理有毒或不足的标记训练数据时显示显著的优势.
结论:
- 开发的无监督深度假冒检测器为数据稀缺和对抗性攻击的挑战提供了强大的解决方案.
- 这种方法提高了深度假冒检测在现实场景中的可靠性和适用性.
- 该方法为监督技术提供了强大的替代方案,特别是在安全敏感的环境中.
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