On Lyapunov exponents and adversarial perturbation

Vinay Uday Prabhu; Nishant Desai; John Whaley

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arXiv · 1802.06927

On Lyapunov exponents and adversarial perturbation

Vinay Uday Prabhu·Nishant Desai·John Whaley

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Abstract

In this paper, we would like to disseminate a serendipitous discovery involving Lyapunov exponents of a 1-D time series and their use in serving as a filtering defense tool against a specific kind of deep adversarial perturbation. To this end, we use the state-of-the-art CleverHans library to generate adversarial perturbations against a standard Convolutional Neural Network (CNN) architecture trained on the MNIST as well as the Fashion-MNIST datasets. We empirically demonstrate how the Lyapunov exponents computed on the flattened 1-D vector representations of the images served as highly discriminative features that could be to pre-classify images as adversarial or legitimate before feeding the image into the CNN for classification. We also explore the issue of possible false-alarms when the input images are noisy in a non-adversarial sense.

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Vinay Uday Prabhu, Nishant Desai, John Whaley. 2018-02-20. On Lyapunov exponents and adversarial perturbation. https://arxiv.org/abs/1802.06927

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