In this work, we propose a robust framework that employs adversarially robust training to safeguard the machine learning models against perturbed testing data. We achieve this by incorporating the worst-case additive adversarial error within a fixed budget for each sample during model estimation. Our main focus is to provide a plug-and-play solution that can be incorporated in the existing machine learning algorithms with minimal changes. To that end, we derive the closed-form ready-to-use solution for several widely used loss functions with a variety of norm constraints on adversarial perturbation. Finally, we validate our approach by showing significant performance improvement on real-world datasets for supervised problems such as regression and classification, as well as for unsupervised problems such as matrix completion and learning graphical models, with very little computational overhead.

翻译：在这项工作中,我们提出一个强有力的框架,利用敌对式强力培训,保护机器学习模式不受干扰测试数据的影响。我们通过将最坏的附加对抗错误纳入模型估计期间每个样本的固定预算来实现这一点。我们的主要重点是提供一个插头和游戏解决方案,可以纳入现有的机器学习算法中,但变化最小。为此,我们为几个广泛使用的损失功能找到封闭式现用解决方案,这些功能对对抗性扰动有着各种规范限制。最后,我们验证了我们的方法,在真实世界的数据集中,对诸如回归和分类等受监督的问题以及诸如矩阵完成和学习图形模型等不受监督的问题表现出显著的性能改进,而计算间接费用很少。