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Facial forgery detection is a crucial but extremely challenging topic, withthe fast development of forgery techniques making the synthetic artefact highlyindistinguishable. Prior works show that by mining both spatial and frequencyinformation the forgery detection performance of deep learning models can bevastly improved. However, leveraging multiple types of information usuallyrequires more than one branch in the neural network, which makes the modelheavy and cumbersome. Knowledge distillation, as an important technique forefficient modelling, could be a possible remedy. We find that existingknowledge distillation methods have difficulties distilling a dual-branch modelinto a single-branch model. More specifically, knowledge distillation on boththe spatial and frequency branches has degraded performance than distillationonly on the spatial branch. To handle such problem, we propose a noveltwo-in-one knowledge distillation framework which can smoothly merge theinformation from a large dual-branch network into a small single-branchnetwork, with the help of different dedicated feature projectors and thegradient homogenization technique. Experimental analysis on two datasets,FaceForensics++ and Celeb-DF, shows that our proposed framework achievessuperior performance for facial forgery detection with much fewer parameters.

Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection