Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input.
Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries.
Therefore, they are not suitable for real-world systems where the maximum query number is limited due to cost.
We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction.
We demonstrate empirically that our method can achieve comparable success rates with 2-5 times fewer queries compared to previous state-of-the-art black-box attacks.
Deep learning algorithms are widely deployed in many real-world systems and are increasingly being used for tasks, ranging from identity verification , to financial services (Heaton et al., 2017) to autonomous driving (Bojarski et al., 2016) .
However, even the most accurate deep learning models can be easily deceived by perturbations which are visually imperceptible to the human eye (Szegedy et al., 2013; Carlini et al., 2016) .
The growing costs and risks associated with the potential model failures has led to the importance of studying adversarial attacks, both in assessing their robustness and their ability to detect such attacks.
In this paper we focus on highly practical adversarial attacks that fulfill the following two criteria.
First, the attack is designed for a black-box setting because in real-world examples, the attacker would normally have no knowledge of the target deep learning model and can only interact with the model by querying it.
Second, query efficiency is highly prioritised because in practical cases where the damage caused by the attack is high, the query budget available to the attacker will be highly limited due to the risk of being detected by the defence system or other high inherent costs (monetary or computational) of model evaluations.
Despite of the large array of adversarial attacks proposed in the literature, many of them are whitebox approaches that assume full access to the target model architecture and the ability of performing back-propagation to get gradient information (Moosavi-Dezfooli et al., 2016; Kurakin et al., 2016; Gu & Rigazio, 2014; Goodfellow et al., 2014; Carlini & Wagner, 2017) .
On the other hand, for black-box attacks, there are various techniques that have been used which do not require access to the model architecture.
One class of methods trains a white-box substitute model and attacks the target model with adversarial examples that successfully fool the substitute (Papernot et al., 2017) .
However, this type of method requires the availability of the original training data or large query data to train the substitute network and the performance is often limited by the mismatch between the substitute and the target models (Su et al., 2018) .
The second class of black-box attacks, which show better empirical performance than the substitute model approaches, numerically estimate the gradient of the target model by repeatedly querying it (Chen et al., 2017; Ilyas et al., 2018; Tu et al., 2018) and attack with the estimated gradient.
Although various techniques are employed to increase the query efficiency for the gradient estimation, they need an excessively large query budget to achieve a successful attack (Alzantot et al., 2018) .
Another line of work removes the need for gradient estimation and uses decision-based techniques (Brendel et al., 2017) or genetic algorithms (Alzantot et al., 2018) to generate adversarial examples.
One popular technique that is adopted by many black-box attacks (Chen et al., 2017; Alzantot et al., 2018; Tu et al., 2018) to significantly improve the query efficiency is to search for adversarial perturbations in a low-dimensional latent space (search dimensionality reduction).
However, learning the effective dimensionality of the latent search space can be challenging by itself, and has not been investigated by the prior works to the best of our knowledge.
In light of the above limitations, we propose a query efficient black-box attack that iteratively optimises over both the adversarial perturbation and the effective dimensionality of the latent search space.
Our main contributions are summarised as follows:
• We introduce a novel gradient-free black-box attack method, BayesOpt attack, which uses Bayesian optimisation with Gaussian process surrogate models to find the effective adversarial example and is capable of dealing with high-dimensional image inputs.
• We proposes a Bayesian technique which learns the optimal degree of search dimensionality reduction by harnessing our statistical surrogate and information from query data.
This technique can be incorporated naturally into our attack procedure, leading to efficient optimisation over both adversarial perturbation and the latent search space dimension.
• We empirically demonstrate that under the L ∞ constraint, our proposed attack method can achieve comparable success rate with about 2 to 5 times fewer model queries in comparison to current state-of-the-art query-efficient black box attack methods.
We introduce a new black-box adversarial attack which leverages Bayesian optimisation to find successful adversarial perturbations with high query efficiency.
We also improve our attack by adopting an additive surrogate structure to ease the optimisation challenge over the typically high-dimensional task.
Moreover, we take full advantage of our statistical surrogate model and the available query data to learn the optimal degree of dimension reduction for the search space via Bayesian model selection.
In comparison to several existing black-box attack methods, our BayesOpt attacks can achieve high success rates with 2-5 times fewer queries while still producing adversarial examples that are closer to the original image (in terms of average L 2 distance).
We believe our BayesOpt attacks can be a competitive alternative for accessing the model robustness, especially in real-world applications where the available query budget is highly limited and the model evaluation is expensive or risky.
We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction.