Abstract
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 this https URL.
Abstract (translated)
在这篇论文中，我们提出了一种专为电子了解您的客户(eKYC)系统设计的深度伪造检测算法。为了确保eKYC系统能够抵御深度伪造攻击，开发一种既能识别面部替换也能识别面部再现，并且在图像退化情况下依然稳健的深度伪造检测器至关重要。通过以下三个关键贡献，我们解决了这些挑战： 1. 我们的方法通过对由面部识别模型提取的身份向量进行时间不一致性的检测来评估视频的真实性，从而全面地检测出面部替换和面部再现。 2. 除了处理视频输入之外，该算法还利用一个注册图像（假设为真实），计算输入视频与注册图像之间身份差异，显著提高了检测准确性。 3. 我们发现使用在更大数据集上训练的面部特征提取器可以提高检测性能，并增强其对各种形式图像退化的鲁棒性。 我们的实验结果表明，我们提出的方法能够准确且全面地检测出面部替换和面部再现，并且对于各种未见过的图像降质情况具有较强的鲁棒性。我们的源代码可在[此处](https://this https URL)公开获取。
URL
https://arxiv.org/abs/2507.22601
