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When the Differences in Frequency Domain are Compensated: Understanding
  and Defeating Modulated Replay Attacks on Automatic Speech Recognition

When the Differences in Frequency Domain are Compensated: Understanding and Defeating Modulated Replay Attacks on Automatic Speech Recognition

1 September 2020
Shu Wang
Jiahao Cao
Xu He
Kun Sun
Qi Li
    AAML
ArXiv (abs)PDFHTML

Papers citing "When the Differences in Frequency Domain are Compensated: Understanding and Defeating Modulated Replay Attacks on Automatic Speech Recognition"

13 / 13 papers shown
Title
Towards Vulnerability Analysis of Voice-Driven Interfaces and
  Countermeasures for Replay
Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replay
K. Malik
Hafiz Malik
Roland Baumann
AAML
46
34
0
13 Apr 2019
Practical Hidden Voice Attacks against Speech and Speaker Recognition
  Systems
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems
H. Abdullah
Washington Garcia
Christian Peeters
Patrick Traynor
Kevin R. B. Butler
Joseph N. Wilson
AAML
49
168
0
18 Mar 2019
Protecting Voice Controlled Systems Using Sound Source Identification
  Based on Acoustic Cues
Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues
Yuan Gong
C. Poellabauer
AAML
47
27
0
16 Nov 2018
Adversarial Attacks Against Automatic Speech Recognition Systems via
  Psychoacoustic Hiding
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding
Lea Schonherr
Katharina Kohls
Steffen Zeiler
Thorsten Holz
D. Kolossa
AAML
77
289
0
16 Aug 2018
Understanding and Mitigating the Security Risks of Voice-Controlled
  Third-Party Skills on Amazon Alexa and Google Home
Understanding and Mitigating the Security Risks of Voice-Controlled Third-Party Skills on Amazon Alexa and Google Home
Yi Yu
Xianghang Mi
Xuan Feng
Xiaofeng Wang
Yuan Tian
Feng Qian
28
74
0
03 May 2018
CommanderSong: A Systematic Approach for Practical Adversarial Voice
  Recognition
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
Xuejing Yuan
Yuxuan Chen
Yue Zhao
Yunhui Long
Xiaokang Liu
Kai Chen
Shengzhi Zhang
Heqing Huang
Xiaofeng Wang
Carl A. Gunter
AAML
69
355
0
24 Jan 2018
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Nicholas Carlini
D. Wagner
AAML
97
1,083
0
05 Jan 2018
Crafting Adversarial Examples For Speech Paralinguistics Applications
Crafting Adversarial Examples For Speech Paralinguistics Applications
Yuan Gong
C. Poellabauer
AAML
62
122
0
09 Nov 2017
DolphinAtack: Inaudible Voice Commands
DolphinAtack: Inaudible Voice Commands
Guoming Zhang
Chen Yan
Xiaoyu Ji
Taimin Zhang
Tianchen Zhang
Wenyuan Xu
AAML
43
695
0
31 Aug 2017
Inaudible Voice Commands
Inaudible Voice Commands
Liwei Song
Prateek Mittal
AAML
34
66
0
24 Aug 2017
AuDroid: Preventing Attacks on Audio Channels in Mobile Devices
AuDroid: Preventing Attacks on Audio Channels in Mobile Devices
Giuseppe Petracca
Yuqiong Sun
Ahmad Atamli-Reineh
Trent Jaeger
AAML
74
78
0
01 Apr 2016
STC Anti-spoofing Systems for the ASVspoof 2015 Challenge
STC Anti-spoofing Systems for the ASVspoof 2015 Challenge
Sergey Novoselov
Alexander Kozlov
G. Lavrentyeva
K. Simonchik
Vadim Shchemelinin
59
76
0
29 Jul 2015
Your Voice Assistant is Mine: How to Abuse Speakers to Steal Information
  and Control Your Phone
Your Voice Assistant is Mine: How to Abuse Speakers to Steal Information and Control Your Phone
Wenrui Diao
Xiangyu Liu
Zhe Zhou
Kehuan Zhang
66
157
0
18 Jul 2014
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