Learning Pairwise Interaction for Generalizable DeepFake Detection
Ying Xu, Kiran Raja, Luisa Verdoliva, Marius Pedersen

TL;DR
This paper introduces MCX-API, a novel deepfake detection method that leverages pairwise learning and multi-color space features, demonstrating superior generalization across unseen DeepFake schemes and datasets.
Contribution
The paper proposes MCX-API, a new approach combining pairwise learning and multi-channel color representations to improve deepfake detection generalization.
Findings
Achieves 98.48% BOSC accuracy on FF++ dataset
Attains 90.87% BOSC accuracy on CelebDF dataset
Outperforms state-of-the-art detectors in open-set scenarios
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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Digital Media Forensic Detection · Image Enhancement Techniques
Methodsfail · Pointwise Convolution · Average Pooling · Residual Connection · 1x1 Convolution · Dense Connections · Global Average Pooling · Max Pooling · Convolution · Depthwise Convolution
