用注册图提升eKYC反深度伪造能力，精准识别换脸与重演。
Robust Deepfake Detection for Electronic Know Your Customer Systems Using Registered Images
- 通过分析人脸识别模型提取的身份向量时序不一致，检测换脸与重演。
- 结合注册真实图像计算身份差异，显著提升检测准确率。
- 大模型训练特征提取器增强抗图像退化能力，适合金融安防场景。
本文提出一种专为电子实名认证（eKYC）系统设计的深度伪造检测算法。为保障eKYC系统免受深度伪造攻击，需具备同时识别面部换脸与重演，并对图像退化具有鲁棒性的能力。我们通过三项关键贡献实现：(1) 通过检测人脸识别模型提取的身份向量在视频中的时序不一致性，全面识别换脸与重演；(2) 在处理视频输入的同时，利用已注册的真实图像（假设为真实）计算输入视频与注册图像间的身份差异，显著提升检测精度；(3) 发现使用在更大数据集上训练的面部特征提取器可同时提升检测性能和对图像退化的鲁棒性。实验表明，所提方法能准确检测换脸与重演，且对多种未见图像退化形式保持鲁棒。源代码公开于 https://github.com/TaikiMiyagawa/DeepfakeDetection4eKYC。
原文摘要 · Abstract (English)
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.
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