论文标题
面部识别是否可以免受可实现的攻击？
Is Face Recognition Safe from Realizable Attacks?
论文作者
论文摘要
面部识别是生物特征验证的一种流行形式，由于其广泛使用，攻击也变得越来越普遍。最近的研究表明，面部识别系统容易受到攻击，并可能导致对面部的错误识别。有趣的是，这些攻击大多数都是白框，或者它们以不可实现的方式操纵面部图像。在本文中，我们提出了一种攻击方案，攻击者可以在其上生成逼真的合成面部图像，并从物理上意识到他的脸上以攻击黑框的面部识别系统。全面的实验和分析表明，在攻击者面对面实现的微妙扰动可以成功地对黑盒设置中最新的面部识别系统产生攻击。我们的研究暴露了面部识别系统对可实现的黑盒攻击产生的潜在脆弱性。
Face recognition is a popular form of biometric authentication and due to its widespread use, attacks have become more common as well. Recent studies show that Face Recognition Systems are vulnerable to attacks and can lead to erroneous identification of faces. Interestingly, most of these attacks are white-box, or they are manipulating facial images in ways that are not physically realizable. In this paper, we propose an attack scheme where the attacker can generate realistic synthesized face images with subtle perturbations and physically realize that onto his face to attack black-box face recognition systems. Comprehensive experiments and analyses show that subtle perturbations realized on attackers face can create successful attacks on state-of-the-art face recognition systems in black-box settings. Our study exposes the underlying vulnerability posed by the Face Recognition Systems against realizable black-box attacks.
