AAAI Publications, Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence

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Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks
Yi Han, Benjamin Rubinstein

Last modified: 2018-06-20


Despite the widespread use of machine learning in adversarial settings such as computer security, recent studies have demonstrated vulnerabilities to evasion attacks---carefully crafted adversarial samples that closely resemble legitimate instances, but cause misclassification. In this paper, we examine the adequacy of the leading approach to generating adversarial samples---the gradient-descent approach. In particular (1) we perform extensive experiments on three datasets, MNIST, USPS and Spambase, in order to analyse the effectiveness of the gradient-descent method against non-linear support vector machines, and conclude that carefully reduced kernel smoothness can significantly increase robustness to the attack; (2) we demonstrate that separated inter-class support vectors lead to more secure models, and propose a quantity similar to margin that can efficiently predict potential susceptibility to gradient-descent attacks, before the attack is launched; and (3) we design a new adversarial sample construction algorithm based on optimising the multiplicative ratio of class decision functions.


Adversarial learning; Evasion attacks; Gradient descent; RBF SVM

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