  Recent work has shown how easily white-box adversarial attacks can be applied
to state-of-the-art image classifiers. However, real-life scenarios resemble
more the black-box adversarial conditions, lacking transparency and usually
imposing natural, hard constraints on the query budget.
  We propose $\textbf{EvoBA}$, a black-box adversarial attack based on a
surprisingly simple evolutionary search strategy. $\textbf{EvoBA}$ is
query-efficient, minimizes $L_0$ adversarial perturbations, and does not
require any form of training.
  $\textbf{EvoBA}$ shows efficiency and efficacy through results that are in
line with much more complex state-of-the-art black-box attacks such as
$\textbf{AutoZOOM}$. It is more query-efficient than $\textbf{SimBA}$, a simple
and powerful baseline black-box attack, and has a similar level of complexity.
Therefore, we propose it both as a new strong baseline for black-box
adversarial attacks and as a fast and general tool for gaining empirical
insight into how robust image classifiers are with respect to $L_0$ adversarial
perturbations.
  There exist fast and reliable $L_2$ black-box attacks, such as
$\textbf{SimBA}$, and $L_{\infty}$ black-box attacks, such as
$\textbf{DeepSearch}$. We propose $\textbf{EvoBA}$ as a query-efficient $L_0$
black-box adversarial attack which, together with the aforementioned methods,
can serve as a generic tool to assess the empirical robustness of image
classifiers. The main advantages of such methods are that they run fast, are
query-efficient, and can easily be integrated in image classifiers development
pipelines.
  While our attack minimises the $L_0$ adversarial perturbation, we also report
$L_2$, and notice that we compare favorably to the state-of-the-art $L_2$
black-box attack, $\textbf{AutoZOOM}$, and of the $L_2$ strong baseline,
$\textbf{SimBA}$.
