Learning Pairwise Interaction for Generalizable DeepFake Detection
This addresses the challenge of deploying reliable DeepFake detectors in open-set scenarios, though it is incremental as it builds on existing pairwise learning and multi-channel approaches.
The paper tackles the problem of limited generalizability in DeepFake detection across unseen generation types by proposing MCX-API, which achieves 98.48% BOSC accuracy on FF++ and 90.87% on CelebDF datasets, outperforming state-of-the-art methods.
A fast-paced development of DeepFake generation techniques challenge the detection schemes designed for known type DeepFakes. A reliable Deepfake detection approach must be agnostic to generation types, which can present diverse quality and appearance. Limited generalizability across different generation schemes will restrict the wide-scale deployment of detectors if they fail to handle unseen attacks in an open set scenario. We propose a new approach, Multi-Channel Xception Attention Pairwise Interaction (MCX-API), that exploits the power of pairwise learning and complementary information from different color space representations in a fine-grained manner. We first validate our idea on a publicly available dataset in a intra-class setting (closed set) with four different Deepfake schemes. Further, we report all the results using balanced-open-set-classification (BOSC) accuracy in an inter-class setting (open-set) using three public datasets. Our experiments indicate that our proposed method can generalize better than the state-of-the-art Deepfakes detectors. We obtain 98.48% BOSC accuracy on the FF++ dataset and 90.87% BOSC accuracy on the CelebDF dataset suggesting a promising direction for generalization of DeepFake detection. We further utilize t-SNE and attention maps to interpret and visualize the decision-making process of our proposed network. https://github.com/xuyingzhongguo/MCX-API