Adversarial attacks on RNN-based deep learning systems

Automatic Speech Recognition (ASR) systems have been growing in prevalence together with the advancement in deep learning. Built within many Intelligent Voice Control (IVC) systems such as Alexa, Siri and Google Assistant, ASR has become an attractive target for adversarial attacks. In this research...

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Bibliographic Details
Main Author:	Loi, Chii Lek
Other Authors:	Liu Yang
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2020
Subjects:	Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Online Access:	https://hdl.handle.net/10356/137926
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-137926
record_format	dspace
spelling	sg-ntu-dr.10356-1379262020-04-20T00:31:49Z Adversarial attacks on RNN-based deep learning systems Loi, Chii Lek Liu Yang School of Computer Science and Engineering yangliu@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Automatic Speech Recognition (ASR) systems have been growing in prevalence together with the advancement in deep learning. Built within many Intelligent Voice Control (IVC) systems such as Alexa, Siri and Google Assistant, ASR has become an attractive target for adversarial attacks. In this research project, the objective is to create a black-box over-the-air (OTA) attack system that can mutate an audio into its adversarial form with imperceptible difference, such that it will be interpreted as the targeted word by the ASR. In this paper, we demonstrate the feasibility and effectiveness of such an attack system in generating perturbation for the DeepSpeech ASR. Bachelor of Engineering (Computer Science) 2020-04-20T00:31:49Z 2020-04-20T00:31:49Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/137926 en SCSE 19-0319 application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
spellingShingle	Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Loi, Chii Lek Adversarial attacks on RNN-based deep learning systems
description	Automatic Speech Recognition (ASR) systems have been growing in prevalence together with the advancement in deep learning. Built within many Intelligent Voice Control (IVC) systems such as Alexa, Siri and Google Assistant, ASR has become an attractive target for adversarial attacks. In this research project, the objective is to create a black-box over-the-air (OTA) attack system that can mutate an audio into its adversarial form with imperceptible difference, such that it will be interpreted as the targeted word by the ASR. In this paper, we demonstrate the feasibility and effectiveness of such an attack system in generating perturbation for the DeepSpeech ASR.
author2	Liu Yang
author_facet	Liu Yang Loi, Chii Lek
format	Final Year Project
author	Loi, Chii Lek
author_sort	Loi, Chii Lek
title	Adversarial attacks on RNN-based deep learning systems
title_short	Adversarial attacks on RNN-based deep learning systems
title_full	Adversarial attacks on RNN-based deep learning systems
title_fullStr	Adversarial attacks on RNN-based deep learning systems
title_full_unstemmed	Adversarial attacks on RNN-based deep learning systems
title_sort	adversarial attacks on rnn-based deep learning systems
publisher	Nanyang Technological University
publishDate	2020
url	https://hdl.handle.net/10356/137926
_version_	1681057799811891200

Adversarial attacks on RNN-based deep learning systems

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