HIDD: Human-perception-centric Incremental Deepfake Detection
Xiaorong Ma, Jiahe Tian, Yu Cai, Yesheng Chai, Zhaoxing Li, Jiao Dai, Liangjun Zang, Jizhong Han · 2024
Facial manipulation techniques pose a significant societal threat due to the widespread dissemination of deepfake content on the internet. Existing efforts for deepfake detection exhibit inadequate generalization performance when encountering unseen or degraded samples. We attribute this limitation to the overfitting of minor forgery patterns and variations in data distribution among disparate datasets. To tackle this issue, we introduce an innovative human-perception-centric incremental deepfake detection framework to enhance the generalization capabilities of deepfake detection models through continuous learning from a limited set of new samples. Firstly, the model leverages human perceptual salience to discern and comprehend significant artifacts, thereby mitigating overfitting to minor features. Subsequently, in the incremental learning process, we utilize multi-perspective knowledge distillation and a replay strategy to maintain the performance of the old model and minimize the feature distance between old and new samples. This comprehensive approach mitigates feature-level overfitting and addresses distribution differences among various datasets in the incremental phase. We conducted thorough experiments on four benchmark datasets (FF++, DFDC-P, CDF2, and DFD), and the experimental results demonstrate the superior performance of our method.