Detecting Adversarial Examples through Nonlinear Dimensionality Reduction

Authors: Francesco Crecchi, Davide Bacciu, Battista Biggio
Published: 2019-04-30 | Updated: 2019-05-01

Source: https://arxiv.org/abs/1904.13094

PDF: https://arxiv.org/pdf/1904.13094

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敵対的攻撃手法 敵対的サンプル 深層学習技術

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Abstract

Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques. Our empirical findings show that the proposed approach is able to effectively detect adversarial examples crafted by non-adaptive attackers, i.e., not specifically tuned to bypass the detection method. Given our promising results, we plan to extend our analysis to adaptive attackers in future work.