Robust Deepfake Detection for Electronic Know Your Customer Systems Using Registered Images
This work addresses security vulnerabilities in eKYC systems for financial and identity verification, though it appears incremental as it builds on existing deepfake detection methods with specific adaptations.
The paper tackles the problem of deepfake attacks in electronic Know Your Customer (eKYC) systems by developing a detection algorithm that uses temporal inconsistencies and registered images to identify face swapping and reenactment, achieving robust detection against image degradation.
In this paper, we present a deepfake detection algorithm specifically designed for electronic Know Your Customer (eKYC) systems. To ensure the reliability of eKYC systems against deepfake attacks, it is essential to develop a robust deepfake detector capable of identifying both face swapping and face reenactment, while also being robust to image degradation. We address these challenges through three key contributions: (1)~Our approach evaluates the video's authenticity by detecting temporal inconsistencies in identity vectors extracted by face recognition models, leading to comprehensive detection of both face swapping and face reenactment. (2)~In addition to processing video input, the algorithm utilizes a registered image (assumed to be genuine) to calculate identity discrepancies between the input video and the registered image, significantly improving detection accuracy. (3)~We find that employing a face feature extractor trained on a larger dataset enhances both detection performance and robustness against image degradation. Our experimental results show that our proposed method accurately detects both face swapping and face reenactment comprehensively and is robust against various forms of unseen image degradation. Our source code is publicly available https://github.com/TaikiMiyagawa/DeepfakeDetection4eKYC.