# Making targeted black-box evasion attacks effective and efficient

arXiv: https://arxiv.org/abs/1906.03397 · HTML: https://mlsanity.com/paper/1906.03397/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models](https://mlsanity.com/paper/2402.09316/) (2024)
- [Evasion Attacks on Cost-Utility-Based Adversarial Training for Online AutoML in IoT Networks](https://mlsanity.com/paper/2609.31981/) (2026)

## Abstract (arXiv)

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).