FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments. (arXiv:1909.10480v4 [cs.CR] UPDATED)

Web: http://arxiv.org/abs/1909.10480

As advances in Deep Neural Networks (DNNs) demonstrate unprecedented levels
of performance in many critical applications, their vulnerability to attacks is
still an open question. We consider evasion attacks at testing time against
Deep Learning in constrained environments, in which dependencies between
features need to be satisfied. These situations may arise naturally in tabular
data or may be the result of feature engineering in specific application
domains, such as threat detection in cyber security. We propose a general
iterative gradient-based …

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