DevFD : Developmental Face Forgery Detection by Learning Shared and Orthogonal LoRA Subspaces
Tianshuo Zhang, Li Gao, Siran Peng, Xiangyu Zhu, Zhen Lei
The rise of realistic digital face generation/manipulation poses significant social risks. The primary challenge lies in the rapid and diverse evolution of generation techniques, which often outstrip the detection capabilities of existing models. To defend against the ever-evolving new types of forgery, we need to enable our model to quickly adapt to new domains with limited computation and data, while avoiding forgetting previously learned forgery types. In this work, we posit that genuine facial samples are abundant and relatively stable in acquisition methods, while forgery faces continuously evolve with the iteration of manipulation techniques. Given the practical infeasibility of exhaustively collecting all forgery variants, we frame face forgery detection as a continual learning problem and allow the model to scale in complexity as new forgery types emerge. Specifically, we employ a Developmental Mixture of Experts (MoE) architecture, utilizing LoRA models as the individual experts, allocating the experts into two groups: a Real-LoRA to refine the real face knowledge modeled by the backbone and Fake-LoRAs to capture incremental fake face information from different types for each sub-task. To prevent catastrophic forgetting, we ensure that the learning direction of Fake-LoRAs is orthogonal to the established subspace. Moreover, we integrate orthogonal gradients into the orthogonal loss of Fake-LoRAs to alleviate the interference of gradients on previously learned tasks during the early training phase. Experimental results under both the datasets and manipulation types incremental protocols demonstrate the effectiveness of our method.