  The misuse of deepfake technology by malicious actors poses a potential
threat to nations, societies, and individuals. However, existing methods for
detecting deepfakes primarily focus on uncompressed videos, such as noise
characteristics, local textures, or frequency statistics. When applied to
compressed videos, these methods experience a decrease in detection performance
and are less suitable for real-world scenarios. In this paper, we propose a
deepfake video detection method based on 3D spatiotemporal trajectories.
Specifically, we utilize a robust 3D model to construct spatiotemporal motion
features, integrating feature details from both 2D and 3D frames to mitigate
the influence of large head rotation angles or insufficient lighting within
frames. Furthermore, we separate facial expressions from head movements and
design a sequential analysis method based on phase space motion trajectories to
explore the feature differences between genuine and fake faces in deepfake
videos. We conduct extensive experiments to validate the performance of our
proposed method on several compressed deepfake benchmarks. The robustness of
the well-designed features is verified by calculating the consistent
distribution of facial landmarks before and after video compression.Our method
yields satisfactory results and showcases its potential for practical
applications.
