  We introduce a new black-box attack achieving state of the art performances.
Our approach is based on a new objective function, borrowing ideas from
$\ell_\infty$-white box attacks, and particularly designed to fit
derivative-free optimization requirements. It only requires to have access to
the logits of the classifier without any other information which is a more
realistic scenario. Not only we introduce a new objective function, we extend
previous works on black box adversarial attacks to a larger spectrum of
evolution strategies and other derivative-free optimization methods. We also
highlight a new intriguing property that deep neural networks are not robust to
single shot tiled attacks. Our models achieve, with a budget limited to
$10,000$ queries, results up to $99.2\%$ of success rate against InceptionV3
classifier with $630$ queries to the network on average in the untargeted
attacks setting, which is an improvement by $90$ queries of the current state
of the art. In the targeted setting, we are able to reach, with a limited
budget of $100,000$, $100\%$ of success rate with a budget of $6,662$ queries
on average, i.e. we need $800$ queries less than the current state of the art.
