论文标题
Deltabound攻击：低查询制度中有效的基于决策的攻击
DeltaBound Attack: Efficient decision-based attack in low queries regime
论文作者
论文摘要
深层神经网络和其他机器学习系统尽管非常强大并且能够以高准确的方式做出预测，但容易受到对抗性攻击的影响。我们提出了Deltabound攻击：具有$ \ ell_2 $ norm界面扰动的硬标签设置中的一种新颖，有力的攻击。在这种情况下，攻击者只能访问模型的TOP-1预测标签，因此可以应用于现实世界设置，例如远程API。这是一个复杂的问题，因为攻击者对模型的信息很少。因此，文献中存在的大多数其他技术都需要大量攻击单个示例的查询。相反，这项工作主要集中在低查询制度中对攻击力量的评估$ \ leq 1000 $查询），并在硬标签设置中使用$ \ ell_2 $ norm。我们发现，Deltabound攻击的性能也比当前的最新攻击更好，同时在各种模型之间保持竞争力。此外，我们不仅可以针对深层神经网络，而且还评估非深度学习模型，例如增强决策树和多项式幼稚贝叶斯。
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.
