  Many adversarial attacks and defenses have recently been proposed for Deep
Neural Networks (DNNs). While most of them are in the white-box setting, which
is impractical, a new class of query-based hard-label (QBHL) black-box attacks
pose a significant threat to real-world applications (e.g., Google Cloud,
Tencent API). Till now, there has been no generalizable and practical approach
proposed to defend against such attacks.
  This paper proposes and evaluates PredCoin, a practical and generalizable
method for providing robustness against QBHL attacks. PredCoin poisons the
gradient estimation step, an essential component of most QBHL attacks. PredCoin
successfully identifies gradient estimation queries crafted by an attacker and
introduces uncertainty to the output. Extensive experiments show that PredCoin
successfully defends against four state-of-the-art QBHL attacks across various
settings and tasks while preserving the target model's overall accuracy.
  PredCoin is also shown to be robust and effective against several
defense-aware attacks, which may have full knowledge regarding the internal
mechanisms of PredCoin.
