论文标题
确定面部伪造检测和分类的节奏模式
Identifying Rhythmic Patterns for Face Forgery Detection and Categorization
论文作者
论文摘要
随着甘恩的出现，面部伪造技术被严重滥用。即将实现准确的面部伪造检测。受PPG信号的远程光插图学（RPPG）的启发，对应于面部视频中心跳引起的肤色的周期性变化，我们观察到，尽管在伪造过程中不可避免地会丢失PPG信号，但在伪造视频中仍然存在着具有独特的节奏模式的PPG信号，取决于其具有独特的节奏模式。在这一关键观察中，我们提出了一个面部伪造检测和分类的框架，包括：1）用于PPG信号过滤的空间滤波网络（STFNET），以及2）用于PPG信号约束和交互的时空交互网络（stinet）。此外，通过深入了解伪造方法的产生，我们进一步提出了源入源和源中融合，以提高框架的性能。总体而言，广泛的实验证明了我们方法的优势。
With the emergence of GAN, face forgery technologies have been heavily abused. Achieving accurate face forgery detection is imminent. Inspired by remote photoplethysmography (rPPG) that PPG signal corresponds to the periodic change of skin color caused by heartbeat in face videos, we observe that despite the inevitable loss of PPG signal during the forgery process, there is still a mixture of PPG signals in the forgery video with a unique rhythmic pattern depending on its generation method. Motivated by this key observation, we propose a framework for face forgery detection and categorization consisting of: 1) a Spatial-Temporal Filtering Network (STFNet) for PPG signals filtering, and 2) a Spatial-Temporal Interaction Network (STINet) for constraint and interaction of PPG signals. Moreover, with insight into the generation of forgery methods, we further propose intra-source and inter-source blending to boost the performance of the framework. Overall, extensive experiments have proved the superiority of our method.
