Improving Adversarial Robustness via Promoting Ensemble Diversity
This addresses the security issue of adversarial attacks for users of ensemble models in machine learning, but it is incremental as it builds on existing defense methods.
The paper tackles the problem of adversarial vulnerability in deep neural network ensembles by proposing a method to promote ensemble diversity, which improves robustness while maintaining state-of-the-art accuracy on normal examples.
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.