Making targeted black-box evasion attacks effective and efficient
This work addresses the vulnerability of real-world prediction APIs to more efficient adversarial attacks, making black-box evasion significantly easier and more practical for attackers, though it is incremental in improving existing attack methods.
The paper tackles the problem of optimizing query budgets for targeted black-box evasion attacks on deep neural networks, showing that new attack strategies using substitute models achieve similar effectiveness as previous methods but require up to three orders of magnitude fewer queries, reducing the need from about 20,000 to approximately 500 queries against real-world APIs like Google Cloud Vision.
We investigate how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks in a black-box setting. We formalize the problem setting and systematically evaluate what benefits the adversary can gain by using substitute models. We show that there is an exploration-exploitation tradeoff in that query efficiency comes at the cost of effectiveness. We present two new attack strategies for using substitute models and show that they are as effective as previous query-only techniques but require significantly fewer queries, by up to three orders of magnitude. We also show that an agile adversary capable of switching through different attack techniques can achieve pareto-optimal efficiency. We demonstrate our attack against Google Cloud Vision showing that the difficulty of black-box attacks against real-world prediction APIs is significantly easier than previously thought (requiring approximately 500 queries instead of approximately 20,000 as in previous works).