Audio-Visual Deepfake Detection via Multi-Teacher Distillation of Content and Semantic Consistency

The advent of deepfake technology enables the synthesis of highly realistic audio-visual content, posing severe challenges such as identity impersonation and public opinion manipulation. Existing detection methods primarily focus on unimodal analysis or shallow consistency modeling, making it difficult to effectively address cross-modal forgeries and well-synchronized fake samples. These limitations result in poor generalization and high misclassification rates. To tackle these issues, this paper proposes an innovative multi-teacher knowledge distillation detection framework based on content consistency and semantic consistency. By leveraging high-level supervision from speech recognition and semantic comprehension, the framework guides the detection model to simultaneously learn cross-modal alignment in terms of both content and semantics. Additionally, a Transformer-based architecture is integrated to enhance the audio temporal modeling capability of the detection model, improving its ability to capture long-term temporal dependencies in deepfake speech. Comprehensive evaluations conducted on the FakeAVCeleb dataset demonstrate that the proposed method outperforms existing methods in terms of accuracy and AUC, while also exhibiting fewer parameters with higher computational efficiency, highlighting strong practical applicability.