Variant inconsistency attack (via) as a simple and effective adversarial attack method

Abstract

The present disclosure relates to techniques for using variant inconsistency attack (VIA) as a simple and effective adversarial attack method to create useful adversarial examples for adversarial training of machine-learning models. In one particular aspect, a method is provided that includes obtaining a set of input examples for attacking a machine-learning model (the set of examples do not have corresponding labels), modifying an example from the set of examples in a utility preserving manner to generate a pair of modified examples, attacking the machine-learning model with the pair of modified examples in order generate a pair of predictions for the pair of modified examples, comparing the pair of predictions to determine whether the pair of predictions are the same or different, and in response to the pair of predictions being different, adding the pair of modified examples to a set of adversarial examples.