Clustering algorithms play a fundamental role as tools in decision-making and sensible automation processes. Due to the widespread use of these applications, a robustness analysis of this family of algorithms against adversarial noise has become imperative. To the best of our knowledge, however, only a few works have currently addressed this problem. In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We formulate the problem as a constrained minimization program, general in its structure and customizable by the attacker according to her capability constraints. We do not assume any information about the internal structure of the victim clustering algorithm, and we allow the attacker to query it as a service only. In the absence of any derivative information, we perform the optimization with a custom approach inspired by the Abstract Genetic Algorithm (AGA). In the experimental part, we demonstrate the sensibility of different single and ensemble clustering algorithms against our crafted adversarial samples on different scenarios. Furthermore, we perform a comparison of our algorithm with a state-of-the-art approach showing that we are able to reach or even outperform its performance. Finally, to highlight the general nature of the generated noise, we show that our attacks are transferable even against supervised algorithms such as SVMs, random forests, and neural networks.
翻译:集群算法作为决策和明智自动化过程中的工具,发挥着基本作用。由于这些应用的广泛使用,因此,必须对这些应用进行稳健性分析。然而,据我们所知,目前只有几部工程处理了这一问题。为了填补这一空白,我们提议在这项工作中,用黑箱对抗性攻击来制作对抗性抽样以测试组合算法的稳健性。我们把这个问题当作一个限制最小化程序来设计,一般地在结构上,并且根据攻击者的能力限制来定制。我们不假定任何关于受害者组合算法内部结构的信息,我们允许攻击者仅查询它为一项服务。在没有任何衍生信息的情况下,我们利用抽象遗传算法(AGA)所启发的定制方法进行优化。在实验部分,我们展示了不同单一和混合的组合算法与我们所设计的随机性对不同情景的对抗性标本的模型的敏感性。此外,我们把我们的算法与一个州-艺术组合算法相比,我们甚至可以将它作为一个服务对象进行对比,我们最终显示我们所生成的可移动的系统。