  In spite of the successful application in many fields, machine learning
models today suffer from notorious problems like vulnerability to adversarial
examples. Beyond falling into the cat-and-mouse game between adversarial attack
and defense, this paper provides alternative perspective to consider
adversarial example and explore whether we can exploit it in benign
applications. We first attribute adversarial example to the human-model
disparity on employing non-semantic features. While largely ignored in
classical machine learning mechanisms, non-semantic feature enjoys three
interesting characteristics as (1) exclusive to model, (2) critical to affect
inference, and (3) utilizable as features. Inspired by this, we present brave
new idea of benign adversarial attack to exploit adversarial examples for
goodness in three directions: (1) adversarial Turing test, (2) rejecting
malicious model application, and (3) adversarial data augmentation. Each
direction is positioned with motivation elaboration, justification analysis and
prototype applications to showcase its potential.
