Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation

The generalization capability of deepfake detectors is crucial for real-world applications. Data augmentation to generate synthetic fake faces has served as an effective strategy to enhance generalization. Interestingly, current state-of-the-art (SoTA) methods rely on fixed augmentation strategies, raising a fundamental question: Can a single static augmentation approach suffice, or does the diversity of forgery features necessitate dynamic strategies? We argue that existing methods overlook the evolving complexity of real-world forgery patterns, such as facial warping, expression manipulation, and compression artifacts, which cannot be fully simulated by fixed policies. To bridge this gap, we propose CRDA (Curriculum Reinforcement-Learning Data Augmentation), a novel framework that guides the detector to progressively master multi-domain forgery features from simple to complex. CRDA synthesizes augmented samples using a configurable pool of forgery operations and dynamically generates adversarial samples tailored to the detector’s current learning state. Key to our approach is the integration of reinforcement learning (RL) and causal inference. To efficiently explore the vast augmentation space, an RL agent dynamically selects augmentation actions based on the detector’s performance, ensuring continuous adaptation to increasingly challenging forgeries. Simultaneously, the agent’s output is designed to introduce variations in action spaces, generating heterogeneous forgery patterns. These variations are guided by causal inference theory, which mitigates spurious correlations by suppressing task-irrelevant biases and enforcing the model to focus on causally invariant features. This integration ensures robust generalization by decoupling synthetic augmentation patterns from the model’s learned representations. Extensive experiments demonstrate that the proposed method significantly improves the generalizability of the detector, achieving superior performance compared to state-of-the-art methods on multiple cross-domain datasets.