Searching the Adversarial Example in the Decision Boundary

Jiang, H., Song, Q. and Le Kernec, J. (2020) Searching the Adversarial Example in the Decision Boundary. In: 5th International Conference on the UK-China Emerging Technologies (UCET 2020), Glasgow, UK, 20-21 Aug 2020, ISBN 9781728194882 (doi:10.1109/UCET51115.2020.9205320)

[img] Text
221500.pdf - Accepted Version

721kB

Abstract

Deep learning technology achieves state of the art result in many computer vision missions. However, some researchers point out that current widely used deep learning architectures are vulnerable to adversarial examples. Adversarial examples are inputs generated by applying small and often imperceptible perturbation to examples in the dataset, such that the perturbed examples can degrade the performance of the deep learning architecture.In the paper, we propose a novel adversarial examples generation method. Adversarial examples generated using this method can have small perturbation and have more diversity compare to adversarial examples generated by other method.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Le Kernec, Dr Julien
Authors: Jiang, H., Song, Q., and Le Kernec, J.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781728194882
Copyright Holders:Copyright © 2020 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record