Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks. Many efforts have been devoted to enhancing the robustness of individual networks and then constructing a straightforward ensemble, e.g., by directly averaging the outputs, which ignores the interaction among networks. This paper presents a new method that explores the interaction among individual networks to improve robustness for ensemble models. Technically, we define a new notion of ensemble diversity in the adversarial setting as the diversity among non-maximal predictions of individual members, and present an adaptive diversity promoting (ADP) regularizer to encourage the diversity, which leads to globally better robustness for the ensemble by making adversarial examples difficult to transfer among individual members. Our method is computationally efficient and compatible with the defense methods acting on individual networks. Empirical results on various datasets verify that our method can improve adversarial robustness while maintaining state-of-the-art accuracy on normal examples.

该论文提出了一种新的方法，通过探索个体网络之间的交互来提高集成模型的抗干扰性，通过定义一种新的集成多样性的概念来在对抗性情境中促进网络多样性，并演示了一种适应性的多样性促进正则化器，以提高集成的鲁棒性。该方法在各种数据集上取得了良好的实验效果，同时保持了在正常情况下达到最新水平的精度。

通过促进集成多样性来提高对抗鲁棒性