Deep neural networks and other machine learning systems, despite being
extremely powerful and able to make predictions with high accuracy, are
vulnerable to adversarial attacks. We proposed the DeltaBound attack: a novel,
powerful attack in the hard-label setting with $\ell_2$ norm bounded
perturbations. In this scenario, the attacker has only access to the top-1
predicted label of the model and can be therefore applied to real-world
settings such as remote API. This is a complex problem since the attacker has
very little information about the model. Consequently, most of the other
techniques present in the literature require a massive amount of queries for
attacking a single example. Oppositely, this work mainly focuses on the
evaluation 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 DeltaBound
attack performs as well and sometimes better than current state-of-the-art
attacks while remaining competitive across different kinds of models. Moreover,
we evaluate our method against not only deep neural networks, but also non-deep
learning models, such as Gradient Boosting Decision Trees and Multinomial Naive
Bayes.

该文章针对深度神经网络在模型攻击方面的问题，提出了一种新的攻击方法 DeltaBound attack，该方法在 L2 范数的约束下对模型进行攻击并取得了与其他攻击方法相当甚至更好的效果。