DevFD: Developmental Face Forgery Detection by Learning Shared and Orthogonal LoRA Subspaces

The rise of realistic digital face generation and 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 develop as new forgery types emerge. Specifically, we employ a Developmental Mixture of Experts (MoE) architecture that uses LoRA models as its individual experts. These experts are organized into two groups: a Real-LoRA to learn and refine knowledge of real faces, and multiple Fake-LoRAs to capture incremental information from different forgery types. 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, preventing gradient interference throughout the training process of each task. Experimental results under both the datasets and manipulation types incremental protocols demonstrate the effectiveness of our method.

Key Contributions

• Developmental Mixture of Experts (MoE) architecture using LoRA experts split into a shared Real-LoRA for genuine faces and orthogonal Fake-LoRAs for incrementally emerging forgery types
• Integrated orthogonal loss that combines subspace orthogonal loss with orthogonal gradient projection to prevent catastrophic forgetting throughout all training stages
• Label-guided localized balancing strategy that softly constrains expert routing to separate real-face modeling from incremental forgery-type adaptation

🛡️ Threat Analysis

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