  As DeepFake video manipulation techniques escalate, posing profound threats,
the urgent need to develop efficient detection strategies is underscored.
However, one particular issue lies with facial images being mis-detected, often
originating from degraded videos or adversarial attacks, leading to unexpected
temporal artifacts that can undermine the efficacy of DeepFake video detection
techniques. This paper introduces a novel method for robust DeepFake video
detection, harnessing the power of the proposed Graph-Regularized Attentive
Convolutional Entanglement (GRACE) based on the graph convolutional network
with graph Laplacian to address the aforementioned challenges. First,
conventional Convolution Neural Networks are deployed to perform spatiotemporal
features for the entire video. Then, the spatial and temporal features are
mutually entangled by constructing a graph with sparse constraint, enforcing
essential features of valid face images in the noisy face sequences remaining,
thus augmenting stability and performance for DeepFake video detection.
Furthermore, the Graph Laplacian prior is proposed in the graph convolutional
network to remove the noise pattern in the feature space to further improve the
performance. Comprehensive experiments are conducted to illustrate that our
proposed method delivers state-of-the-art performance in DeepFake video
detection under noisy face sequences. The source code is available at
https://github.com/ming053l/GRACE.
