Counter-Samples: A Stateless Strategy to Neutralize Black Box
  Adversarial Attacks
Roey Bokobza Yisroel Mirsky
Our paper presents a novel defence against black box attacks, where attackers
use the victim model as an oracle to craft their adversarial examples. Unlike
traditional preprocessing defences that rely on sanitizing input samples, our
stateless strategy counters the attack process itself. For every query we
evaluate a counter-sample instead, where the counter-sample is the original
sample optimized against the attacker's objective. By countering every black
box query with a targeted white box optimization, our strategy effectively
introduces an asymmetry to the game to the defender's advantage. This defence
not only effectively misleads the attacker's search for an adversarial example,
it also preserves the model's accuracy on legitimate inputs and is generic to
multiple types of attacks.
  We demonstrate that our approach is remarkably effective against
state-of-the-art black box attacks and outperforms existing defences for both
the CIFAR-10 and ImageNet datasets. Additionally, we also show that the
proposed defence is robust against strong adversaries as well.