On the Adversarial Robustness of Hierarchical Classification

Deep neural networks (DNNs) have demonstrated remarkable success on various learning problems, but they face a formidable challenge in the form of adversarial attacks. Especially, when dealing with complex classification tasks for numerous classes with a hierarchical structure, the adversarial robustness of a DNN model may drop seriously. In this paper, we investigate the adversarial robustness of DNN models on such complex classification tasks. In response, we propose a two-stage hierarchical classification framework, which is composed of a coarse-grained classifier and a series of fine-grained classifiers. A data correction sampling module is designed between the two stages, in order to mitigate the influence of misclassification caused by the coarse-grained classifier; and a discriminative filter learning module is employed in the fine-grained classification, in order to gain better distinguish abilities among fine-grained categories. Experiments on the well-known dataset CIFAR-100 and a newly-constructed hierarchical dataset mini-ImageNet76 demonstrate that employing a hierarchical framework can effectively improve the model robustness on such complex classification tasks.