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Real-time Over-the-air Adversarial Perturbations for Digital
  Communications using Deep Neural Networks

Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks

20 February 2022
R. Sandler
Peter K. Relich
Cloud Cho
Sean Holloway
    AAML
ArXivPDFHTML

Papers citing "Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks"

12 / 12 papers shown
Title
Double Targeted Universal Adversarial Perturbations
Double Targeted Universal Adversarial Perturbations
Philipp Benz
Chaoning Zhang
Tooba Imtiaz
In So Kweon
AAML
70
48
0
07 Oct 2020
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless
  Signal Classifiers
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
Brian Kim
Y. Sagduyu
Kemal Davaslioglu
T. Erpek
S. Ulukus
AAML
55
115
0
11 May 2020
Evaluating Adversarial Evasion Attacks in the Context of Wireless
  Communications
Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications
Bryse Flowers
R. M. Buehrer
William C. Headley
AAML
71
125
0
01 Mar 2019
Adversarial Examples in RF Deep Learning: Detection of the Attack and
  its Physical Robustness
Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness
S. Kokalj-Filipovic
Rob Miller
AAML
54
31
0
16 Feb 2019
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Meysam Sadeghi
Erik G. Larsson
AAML
40
258
0
23 Aug 2018
Adversarial Examples: Attacks and Defenses for Deep Learning
Adversarial Examples: Attacks and Defenses for Deep Learning
Xiaoyong Yuan
Pan He
Qile Zhu
Xiaolin Li
SILM
AAML
94
1,622
0
19 Dec 2017
Over the Air Deep Learning Based Radio Signal Classification
Over the Air Deep Learning Based Radio Signal Classification
Tim O'Shea
Tamoghna Roy
T. Clancy
74
1,081
0
13 Dec 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection
  Methods
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini
D. Wagner
AAML
126
1,857
0
20 May 2017
An Introduction to Deep Learning for the Physical Layer
An Introduction to Deep Learning for the Physical Layer
Tim O'Shea
J. Hoydis
AI4CE
132
2,198
0
02 Feb 2017
Universal adversarial perturbations
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
P. Frossard
AAML
139
2,527
0
26 Oct 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
540
5,897
0
08 Jul 2016
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
270
14,927
1
21 Dec 2013
1