论文标题
使用快速梯度标志方法对面部识别身份验证系统的攻击分析
Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method
论文作者
论文摘要
代表“您是某物”方案的生物识别验证方法被认为是获得受保护资源的最安全方法。使用机器学习技术的最新攻击要求对生物识别认证进行严重的系统重新评估。本文使用面部识别生物识别验证分析并介绍了快速梯度符号方法（FGSM）攻击。机器学习技术已用于训练和测试模型，该模型可以对不同的人的面孔进行分类和识别，并将其用作进行攻击的目标。此外，案例研究将分析FGSM的实现以及模型通过在攻击中应用此方法所具有的降低性能水平。在训练和攻击模型方面，参数的变化进行了测试结果，从而显示了应用FGSM的效率。
Biometric authentication methods, representing the "something you are" scheme, are considered the most secure approach for gaining access to protected resources. Recent attacks using Machine Learning techniques demand a serious systematic reevaluation of biometric authentication. This paper analyzes and presents the Fast Gradient Sign Method (FGSM) attack using face recognition for biometric authentication. Machine Learning techniques have been used to train and test the model, which can classify and identify different people's faces and which will be used as a target for carrying out the attack. Furthermore, the case study will analyze the implementation of the FGSM and the level of performance reduction that the model will have by applying this method in attacking. The test results were performed with the change of parameters both in terms of training and attacking the model, thus showing the efficiency of applying the FGSM.
