论文标题
针对非线性支持向量机的样本对抗扰动的有效方法
An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines
论文作者
论文摘要
对抗性扰动在各种机器学习模型中都非常关注。在本文中，我们研究了非线性支持向量机（SVM）的样本对抗扰动。由于非线性函数的隐式形式将数据映射到特征空间，因此很难获得对抗性扰动的明确形式。通过探索非线性SVM的特殊属性，我们将攻击非线性SVM的优化问题转换为非线性KKT系统。这样的系统可以通过各种数值方法来解决。数值结果表明，我们的方法在计算对抗扰动方面有效。
Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturbations for nonlinear support vector machines (SVMs). Due to the implicit form of the nonlinear functions mapping data to the feature space, it is difficult to obtain the explicit form of the adversarial perturbations. By exploring the special property of nonlinear SVMs, we transform the optimization problem of attacking nonlinear SVMs into a nonlinear KKT system. Such a system can be solved by various numerical methods. Numerical results show that our method is efficient in computing adversarial perturbations.
