# Black-box Adversarial Attacks with Bayesian Optimization

arXiv: https://arxiv.org/abs/1909.13857 · HTML: https://mlsanity.com/paper/1909.13857/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization](https://mlsanity.com/paper/2206.08575/) (2022)
- [Attacking deep networks with surrogate-based adversarial black-box methods is easy](https://mlsanity.com/paper/2203.08725/) (2022)
- [Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior](https://mlsanity.com/paper/2405.19098/) (2024)
- [Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks](https://mlsanity.com/paper/2111.01714/) (2021)
- [Adversarial Attacks on Graph Classification via Bayesian Optimisation](https://mlsanity.com/paper/2111.02842/) (2021)
- [Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition](https://mlsanity.com/paper/2209.04920/) (2022)
- [Query Attack by Multi-Identity Surrogates](https://mlsanity.com/paper/2105.15010/) (2021)
- [BO-DBA: Query-Efficient Decision-Based Adversarial Attacks via Bayesian Optimization](https://mlsanity.com/paper/2106.02732/) (2021)
- [Neural Predictor for Black-Box Adversarial Attacks on Speech Recognition](https://mlsanity.com/paper/2203.09849/) (2022)
- [Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks](https://mlsanity.com/paper/2309.06438/) (2023)
- [Art-Attack: Black-Box Adversarial Attack via Evolutionary Art](https://mlsanity.com/paper/2203.04405/) (2022)
- [AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation](https://mlsanity.com/paper/2607.11063/) (2026)
- [ATLAS-AL: Adaptive Trust-Region for Latent Adversarial Searches via Active Learning](https://mlsanity.com/paper/2610.07323/) (2026)

## Abstract (arXiv)

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversarial attacks. We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques. Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count. In particular, in low query budget regimes, our proposed method reduces the query count up to 80% with respect to the state of the art methods.