Learning Pairwise Interaction for Generalizable DeepFake Detection

• URL: http://arxiv.org/abs/2302.13288v1
• Date: Sun, 26 Feb 2023 10:39:08 GMT
• Title: Learning Pairwise Interaction for Generalizable DeepFake Detection
• Authors: Ying Xu, Kiran Raja, Luisa Verdoliva, Marius Pedersen
• Abstract summary: A fast-paced development of DeepFake generation techniques challenge the detection schemes designed for known type DeepFakes. 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. Our experiments indicate that our proposed method can generalize better than the state-of-the-art Deepfakes detectors.
• Score: 20.723277551489186
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

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