Adaptive Learning with Augmentation Robustness Validation: Towards Generalizable Face Forgery Detection

The rapid advancement of facial manipulation technologies demands detection systems that can generalize to novel forgery techniques. We identify that Standard Learning, reliant on static datasets and uniform sampling, detrimentally biases models towards specific patterns tied to individual generation techniques, hindering their ability to learn general features. To overcome this, we introduce Adaptive Learning (AL) for face forgery detection, a cyclical framework that simultaneously refines both the detector model and the training data through dynamic sample selection and model optimization. AL’s efficacy hinges on identifying samples rich in generalizable forgery clues. Thus, we propose Augmentation Robustness Validation (ARV) as AL’s core purification engine. ARV exploits the stability of predictions across diverse semantic-preserving augmentations as a reliable proxy for general feature presence: samples that exhibit invariant predictions inherently contain robust manipulation traces. Integrating ARV with AL yields Adaptive Learning with Augmentation Robustness Validation (ALarv). ALarv strategically prioritizes stability-verified samples during iterative training cycles, progressively enhancing the model’s focus on transferable forensic features. Inspired by the architectural advantages of ConvNeXt, we incorporate it into ALarv, forming an effective method, ALarv-ConvNeXt. Extensive experiments demonstrate ALarv-ConvNeXt’s superior generalization performance, including emerging diffusion-based synthetic faces.