Generalized Adversarial Training in Riemannian Space

Zhang, Shufei; Huang, Kaizhu; Zhang, Rui; Hussain, Amir

Abstract

Adversarial examples, referred to as augmented data points generated by imperceptible perturbations of input samples, have recently drawn much attention. Well-crafted adversarial examples may even mislead state-of-the-art deep neural network (DNN) models to make wrong predictions easily. To alleviate this problem, many studies have focused on investigating how adversarial examples can be generated and/or effectively handled. All existing works tackle this problem in the Euclidean space. In this paper, we extend the learning of adversarial examples to the more general Riemannian space over DNNs. The proposed work is important in that (1) it is a generalized learning methodology since Riemmanian space will be degraded to the Euclidean space in a special case; (2) it is the first work to tackle the adversarial example problem tractably through the perspective of Riemannian geometry; (3) from the perspective of geometry, our method leads to the steepest direction of the loss function, by considering the second order information of the loss function. We also provide a theoretical study showing that our proposed method can truly find the descent direction for the loss function, with a comparable computational time against traditional adversarial methods. Finally, the proposed framework demonstrates superior performance over traditional counterpart methods, using benchmark data including MNIST, CIFAR-10 and SVHN.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2019 IEEE International Conference on Data Mining (ICDM)
• Start Date: Nov 8, 2019
• End Date: Nov 11, 2019
• Acceptance Date: Aug 9, 2019
• Online Publication Date: Jan 30, 2020
• Publication Date: Jan 30, 2020
• Deposit Date: Aug 20, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 826-835
• Series Title: IEEE International Conference on Data Mining
• Series ISSN: 2374-8486
• Book Title: 2019 IEEE International Conference on Data Mining (ICDM)
• ISBN: 9781728146041
• DOI: https://doi.org/10.1109/icdm.2019.00093
• Public URL: http://researchrepository.napier.ac.uk/Output/2682293