Papers › Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Francesco Croce, Matthias Hein
The evaluation of robustness against adversarial manipulation of neural networks-based classifiers is mainly tested with empirical attacks as methods for the exact computation, even when available, do not scale to large networks. We propose in this paper a new white-box adversarial attack wrt the lₚ-norms for p ∈{1,2,∞} aiming at finding the minimal perturbation necessary to change the class of a given input. It has an intuitive geometric meaning, yields quickly high quality results, minimizes the size of the perturbation (so that it returns the robust accuracy at every threshold with a single run). It performs better or similar to state-of-the-art attacks which are partially specialized to one lₚ-norm, and is robust to the phenomenon of gradient masking.
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Syntology Ran 4 of 6 code samples harvested from 2 repositories linked to this paper; 2 have no recorded run. Of those that ran: 3 ran · our draft was wrong; 1 ran · fixture could not drive it.
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