Facial forgery detection is a crucial but extremely challenging topic, with the fast development of forgery techniques making the synthetic artefact highly indistinguishable. Prior works show that by mining both spatial and frequency information the forgery detection performance of deep learning models can be vastly improved. However, leveraging multiple types of information usually requires more than one branch in the neural network, which makes the model heavy and cumbersome. Knowledge distillation, as an important technique for efficient modelling, could be a possible remedy. We find that existing knowledge distillation methods have difficulties distilling a dual-branch model into a single-branch model. More specifically, knowledge distillation on both the spatial and frequency branches has degraded performance than distillation only on the spatial branch. To handle such problem, we propose a novel two-in-one knowledge distillation framework which can smoothly merge the information from a large dual-branch network into a small single-branch network, with the help of different dedicated feature projectors and the gradient homogenization technique. Experimental analysis on two datasets, FaceForensics++ and Celeb-DF, shows that our proposed framework achieves superior performance for facial forgery detection with much fewer parameters.
翻译:表面伪造检测是一个至关重要但极具挑战性的主题,因为伪造技术的迅速发展使合成人工制品高度分辨,合成合成人工制品高度分辨,因此是一个至关重要但极具挑战性的主题。先前的工作表明,通过挖掘空间和频率信息,深层学习模型的伪造检测性能可以大大改进。然而,利用多种类型的信息通常需要在神经网络中有一个以上的分支,这使得模型重而繁琐。知识蒸馏作为高效建模的重要技术,可以作为一种可能的补救办法。我们发现,现有知识蒸馏方法难以将双层模型蒸馏成单一部门模型。更具体地说,空间和频率两个分支的知识蒸馏会降低性能,而不是仅仅在空间分支中蒸馏。为了处理这类问题,我们提出了一个新型的双层知识蒸馏框架,可以顺利地将大型双层网络中的信息整合成一个小型的单层网络,在不同的专用地貌投影仪和加速同质技术的帮助下。关于两个数据集的实验性分析,Faceforensiccs++和Ceeb-DF,表明,我们提议的框架的造假化参数的高级性能参数要少得多。