Abstract
Adversarial robustness has become a central goal in deep learning, both in theory and in practice. However, successful methods to improve the adversarial robustness (such as adversarial training) greatly hurt generalization performance on the unperturbed data. This could have a major impact on how achieving adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve accuracy on the unperturbed data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases the standard test error (when there is no adversary) from 4.43% to 12.32%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a standard test error of only 6.45%. With our technique, the relative increase in the standard error for the robust model is reduced from 178.1% to just 45.5%.
Original language | English |
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Title of host publication | AISec'19: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security |
Publisher | ACM |
Pages | 95-103 |
ISBN (Print) | 978-1-4503-6833-9 |
DOIs | |
Publication status | Published - 2019 |
MoE publication type | A4 Conference publication |
Event | ACM Workshop on Artificial Intelligence and Security - London, United Kingdom Duration: 15 Nov 2019 → 15 Nov 2019 Conference number: 12 https://aisec.cc/ |
Workshop
Workshop | ACM Workshop on Artificial Intelligence and Security |
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Abbreviated title | AISec |
Country/Territory | United Kingdom |
City | London |
Period | 15/11/2019 → 15/11/2019 |
Internet address |
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