Adversarial attack algorithms are dominated by penalty methods, which are slow in practice, or more efficient distance-customized methods, which are heavily tailored to the properties of the distance considered. We propose a white-box attack algorithm to generate minimally perturbed adversarial examples based on Augmented Lagrangian principles. We bring several algorithmic modifications, which have a crucial effect on performance. Our attack enjoys the generality of penalty methods and the computational efficiency of distance-customized algorithms, and can be readily used for a wide set of distances. We compare our attack to state-of-the-art methods on three datasets and several models, and consistently obtain competitive performances with similar or lower computational complexity.

翻译：反向攻击算法以惩罚方法为主,而惩罚方法在实践中缓慢,或更高效的远程定制方法,这些方法在很大程度上适合所考虑的距离的特性。我们建议采用白箱攻击算法,以产生以拉格朗加原则为基础的最不受干扰的对抗性实例。我们带来了若干种算法修改,对性能具有关键影响。我们的攻击享有惩罚方法的普遍性和远距离定制算法的计算效率,并且可以很容易地用于广泛的距离。我们将我们的攻击与三种数据集和几种模型的先进方法进行比较,并始终以类似或较低的计算复杂性获得竞争性表现。