基于自我混合方法的深度假视频的定位和检测
Junfeng Xu1, Xintao Liu2, Weiguo Lin3
1Communication University of China, School of Computer & Cyber Sciences, Beijing, 100024, China. junfeng@cuc.edu.cn.
Scientific reports
|January 31, 2025
概括
这项研究引入了一种新的基于空间的训练方法,用于深度假视频检测,不需要假样本. 该方法提高了操纵本地化的准确性,并在不同的数据集中得到很好的概括.
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:
- 深度假冒技术由于越来越现实的操纵视频,造成了重大的社会安全威胁.
- 目前的深度假冒检测方法缺乏一般化,并与精确的操纵定位作斗争.
- 细粒度注释数据集的有限可用性阻碍了对操纵本地化的研究.
研究的目的:
- 提出一种新的基于空间的训练方法,用于深度假视频检测,而不需要假样本.
- 为了提高深度假冒检测的准确性和泛化能力.
- 为了提高深度假视频中操纵区域的精确定位.
主要方法:
- 开发了一种基于空间的训练方法,结合了多部分的局部位移变形和融合.
- 生成多种深度假设特征数据和混合区域标签以提供本地化指导.
- 利用了Swin-Unet模型,用于检测和定位的专用损失功能.
主要成果:
- 拟议的方法有效地模拟真实数据集特征,而不使用假样本.
- 在基准数据集 (FF++,Celeb-DF,DFDC) 上实现了令人满意的检测准确性.
- 在深度假冒视频中证明了操纵区域的准确定位.
结论:
- 新的自我混合方法和Swin-Unet模型对于深度假冒视频检测是有效的.
- 该方法克服了现有方法在概括和本地化方面的局限性.
- 这项研究有助于更强大的深度假冒法医和错误信息预防.
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