论文标题
面部识别对基于gan的面部塑形攻击的鲁棒性
Robustness of Facial Recognition to GAN-based Face-morphing Attacks
论文作者
论文摘要
多年来，脸部变形攻击一直是引起人们关注的原因。研究人员努力保持领先一步，提出了许多创建和检测变形图像的方法。但是，这些检测方法通常被证明是不足的。在这项工作中，我们确定了攻击者在他的武器库中可能已经拥有的两种新的，基于GAN的方法。每种方法均可根据最先进的面部识别（FR）算法进行评估，我们证明，改善FR算法的忠诚度确实会导致降低攻击成功率，而在设置操作性接受阈值时，请考虑使用变形图像。
Face-morphing attacks have been a cause for concern for a number of years. Striving to remain one step ahead of attackers, researchers have proposed many methods of both creating and detecting morphed images. These detection methods, however, have generally proven to be inadequate. In this work we identify two new, GAN-based methods that an attacker may already have in his arsenal. Each method is evaluated against state-of-the-art facial recognition (FR) algorithms and we demonstrate that improvements to the fidelity of FR algorithms do lead to a reduction in the success rate of attacks provided morphed images are considered when setting operational acceptance thresholds.
