Adaptive Consensus Multi-Teacher Distillation for Generalizable Face Forgery Detection

Recent advancements in deep learning have significantly lowered the cost of generating and processing facial images, but this progress has also presented a growing challenge for face forgery detection. Existing detection methods, however, often struggle to generalize effectively to forged samples generated by previously unseen forgery techniques. Additionally, transferring knowledge through knowledge distillation to enhance generalization remains a persistent challenge. To address these issues, this paper proposes Adaptive Consensus Multi-teacher Knowledge Distillation (ACMD), a novel framework aimed at improving the generalization capabilities of face forgery detection models. ACMD leverages multiple teacher models with varying network architectures and introduces a consensus mechanism to resolve knowledge conflicts arising from the diversity of teacher models. It further adapts the assignment of sample-specific distillation weights, based on each teacher model’s prediction confidence. By combining this with feature-based knowledge distillation, the student model gains a deeper understanding of the teacher’s knowledge. Extensive experiments on the DeepfakeBench benchmark demonstrate that ACMD not only outperforms a single teacher model but also achieves state-of-the-art performance in dataset evaluations. Specifically, in a cross-dataset evaluation from FaceForensics++ to CDFv2 and DFDC, ACMD achieves frame-level AUCs of 84.22% and 74.43%, respectively, surpassing all baseline models. Ablation studies further validate the effectiveness of each component and highlight their complementary contributions to the overall detection performance.