Related papers: Collaborative Adversarial Training

Collaborative Adversarial Training

URL: http://arxiv.org/abs/2205.11156v1
Date: Mon, 23 May 2022 09:41:41 GMT
Title: Collaborative Adversarial Training
Authors: Qizhang Li, Yiwen Guo, Wangmeng Zuo, and Hao Chen
Abstract summary: We show that some collaborative examples, nearly perceptually indistinguishable from both adversarial and benign examples, can be utilized to enhance adversarial training. A novel method called collaborative adversarial training (CoAT) is thus proposed to achieve new state-of-the-arts.
Score: 82.25340762659991
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to local non-smoothness and steepness of normally obtained loss landscapes. Training augmented with adversarial examples (a.k.a., adversarial training) is considered as an effective remedy. In this paper, we highlight that some collaborative examples, nearly perceptually indistinguishable from both adversarial and benign examples yet show extremely lower prediction loss, can be utilized to enhance adversarial training. A novel method called collaborative adversarial training (CoAT) is thus proposed to achieve new state-of-the-arts.

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