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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 fitderivative-free optimization requirements. It only requires to have access tothe logits of the classifier without any other information which is a morerealistic scenario. Not only we introduce a new objective function, we extendprevious works on black box adversarial attacks to a larger spectrum ofevolution strategies and other derivative-free optimization methods. We alsohighlight a new intriguing property that deep neural networks are not robust tosingle shot tiled attacks. Our models achieve, with a budget limited to$10,000$ queries, results up to $99.2\%$ of success rate against InceptionV3classifier with $630$ queries to the network on average in the untargetedattacks setting, which is an improvement by $90$ queries of the current stateof the art. In the targeted setting, we are able to reach, with a limitedbudget of $100,000$, $100\%$ of success rate with a budget of $6,662$ querieson average, i.e. we need $800$ queries less than the current state of the art.

Yet another but more efficient black-box adversarial attack: tiling and evolution strategies