Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks

6 Apr 2017 Yi Han Benjamin I. P. Rubinstein

Despite the wide use of machine learning in adversarial settings including 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... (read more)

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