Making targeted black-box evasion attacks effective and efficient

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units


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).


Original languageEnglish
Title of host publicationAISec'19: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventACM Workshop on Artificial Intelligence and Security - London, United Kingdom
Duration: 15 Nov 201915 Nov 2019
Conference number: 12


WorkshopACM Workshop on Artificial Intelligence and Security
Abbreviated titleAISec
CountryUnited Kingdom
Internet address

    Research areas

  • adversarial examples, Neural Networks

ID: 40517925