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

PredCoin: Defense against Query-based Hard-label Attack