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Defending against adversarial attacks in deep neural networks
Author(s): Suya You; C-C Jay Kuo
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Paper Abstract

We focus on defending against adversarial attacks in deep neural networks using signal analysis technology. The method employs a novel signal processing theory as a defense to adversarial perturbations. The method neither modifies the protected network nor requires knowledge of the process for generating adversarial examples. Extensive evaluation experiments demonstrate the efficiency and effectiveness of the proposed adversarial defending method.

Paper Details

Date Published: 10 May 2019
PDF: 6 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061C (10 May 2019); doi: 10.1117/12.2519268
Show Author Affiliations
Suya You, U.S. Army Research Lab. (United States)
C-C Jay Kuo, Univ. of Southern California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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