Limited Budget Adversarial Attack Against Online Image Stream

Hossein Mohasel Arjomandi, Mohammad Khalooei, Maryam Amirmazlaghani

Keywords: Adversarial Attack, Online Image Stream, Threat Model

TL;DR: An adversary wants to attack a limited number of images within a stream of known length to reduce the exposure risk. Also, the adversary wants to maximize the success rate of attacks. We propose an algorithm which yields promising results.

Abstract:

An adversary wants to attack a limited number of images within a stream of known length to reduce
the exposure risk. Also, the adversary wants to maximize the success rate of the performed attacks. We show that with very minimal changes in images data, majority of attacking attempt would fail, however some attempts still lead to succeed. We detail an algorithm that choose the optimal images which lead to successful attack . We apply our approach on MNIST and prove it’s significant outcome compared to the state of the art.