Critical Contour Prior-Guided Graph Learning With Pose Calibration for Identity-Aware Deepfake Detection

Deepfake has recently raised severe public concerns about security issues, such as creating fake news of celebrities. As countermeasures, identity-aware detection methods leverage identity information to expose forged videos by measuring identity consistency between the suspicious input and its reference samples. However, the performance of existing methods suffers from notable degradation due to undesired variations of head poses and capturing environments. In this work, we first conduct a statistical analysis to illustrate the influence of different facial regions for forensic purposes, which infers more reliable identity information is located in critical face regions. Motivated by this analysis, we propose a graph learning-based identity-aware deepfake detection framework considering critical contour prior as guidance. First, feature sampling based on contour landmarks is applied to construct the graph data as the input of our critical contour prior-guided graph attention network (CP-GAT), where a node position prediction task is constructed as auxiliary supervision to explore rich relationships between nodes. To enhance pose-invariant ability, a rotation compensation block is integrated into CP-GAT and trained using a pose-calibrated contrastive learning to extract identity features, which takes high-quality front faces as the calibration goal with a progressively updating selection. Besides, an adversarial node masking-based training strategy is proposed as feature augmentation to further enhance the reliability. During the inference stage, the similarity between identity features of the input sample and its reference samples extracted by the trained CP-GAT is used to obtain the detection result. Extensive experiments are conducted on various face forgery datasets and state-of-the-art methods are compared to verify the superiority of the proposed method in terms of detection capability and robustness.