AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks

• URL: http://arxiv.org/abs/2002.08439v1
• Date: Wed, 19 Feb 2020 20:46:54 GMT
• Title: AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks
• Authors: Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, Peter Chin
• Abstract summary: Deep neural networks have been proliferated rapidly in many security-critical domains such as malware detection and self-driving cars. Conventional defense methods, although shown to be promising, are largely limited by their single-source single-cost nature. We show that the multi-source nature of AdvMS mitigates the performance plateauing issue and the multi-cost nature enables improving robustness at a flexible and adjustable combination of costs.
• Score: 81.45930614122925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware detection and self-driving cars. Conventional defense methods, although shown to be promising, are largely limited by their single-source single-cost nature: The robustness promotion tends to plateau when the defenses are made increasingly stronger while the cost tends to amplify. In this paper, we study principles of designing multi-source and multi-cost schemes where defense performance is boosted from multiple defending components. Based on this motivation, we propose a multi-source and multi-cost defense scheme, Adversarially Trained Model Switching (AdvMS), that inherits advantages from two leading schemes: adversarial training and random model switching. We show that the multi-source nature of AdvMS mitigates the performance plateauing issue and the multi-cost nature enables improving robustness at a flexible and adjustable combination of costs over different factors which can better suit specific restrictions and needs in practice.

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