Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems
Joint Authors
Zhang, Zhaoxin
Liu, Xiaowen
Yang, Lingyu
Huang, Shize
Yang, Xiaolu
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-22
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Trams have increasingly deployed object detectors to perceive running conditions, and deep learning networks have been widely adopted by those detectors.
Growing neural networks have incurred severe attacks such as adversarial example attacks, imposing threats to tram safety.
Only if adversarial attacks are studied thoroughly, researchers can come up with better defence methods against them.
However, most existing methods of generating adversarial examples have been devoted to classification, and none of them target tram environment perception systems.
In this paper, we propose an improved projected gradient descent (PGD) algorithm and an improved Carlini and Wagner (C&W) algorithm to generate adversarial examples against Faster R-CNN object detectors.
Experiments verify that both algorithms can successfully conduct nontargeted and targeted white-box digital attacks when trams are running.
We also compare the performance of the two methods, including attack effects, similarity to clean images, and the generating time.
The results show that both algorithms can generate adversarial examples within 220 seconds, a much shorter time, without decrease of the success rate.
American Psychological Association (APA)
Huang, Shize& Liu, Xiaowen& Yang, Xiaolu& Zhang, Zhaoxin& Yang, Lingyu. 2020. Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1143363
Modern Language Association (MLA)
Huang, Shize…[et al.]. Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1143363
American Medical Association (AMA)
Huang, Shize& Liu, Xiaowen& Yang, Xiaolu& Zhang, Zhaoxin& Yang, Lingyu. Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1143363
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143363