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Deep neural networks and other machine learning systems, despite beingextremely powerful and able to make predictions with high accuracy, arevulnerable to adversarial attacks. We proposed the DeltaBound attack: a novel,powerful attack in the hard-label setting with $\ell_2$ norm boundedperturbations. In this scenario, the attacker has only access to the top-1predicted label of the model and can be therefore applied to real-worldsettings such as remote API. This is a complex problem since the attacker hasvery little information about the model. Consequently, most of the othertechniques present in the literature require a massive amount of queries forattacking a single example. Oppositely, this work mainly focuses on theevaluation of attack's power in the low queries regime $\leq 1000$ queries)with $\ell_2$ norm in the hard-label settings. We find that the DeltaBoundattack performs as well and sometimes better than current state-of-the-artattacks while remaining competitive across different kinds of models. Moreover,we evaluate our method against not only deep neural networks, but also non-deeplearning models, such as Gradient Boosting Decision Trees and Multinomial NaiveBayes.

DeltaBound Attack: Efficient decision-based attack in low queries regime