An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines
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.
Code (0)
등록된 구현이 없습니다.
Tasks
FormSimilar Papers 제목 키워드 기반
Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines
Adversarial perturbations have drawn great attentions in various deep neural networks. Most of them are computed by iterations and cannot be interpreted very well. In contrast, little attentions are paid to basic machine…
Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks
The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, in this paper, we propose a nonlinear radial basis convolution…
image-classificationImage ClassificationAdversarial and Natural Perturbations for General Robustness
In this paper we aim to explore the general robustness of neural network classifiers by utilizing adversarial as well as natural perturbations. Different from previous works which mainly focus on studying the robustness …
Adversarial Purification of Information Masking
Adversarial attacks meticulously generate minuscule, imperceptible perturbations to images to deceive neural networks. Counteracting these, adversarial purification methods seek to transform adversarial input samples int…
Adversarial AttackAdversarial PurificationRobust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to the input. These adversarial perturbations, though imperceptible, can easily mislead the DNN. In this work, we take a control theo…
Robust Design