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AudioGuard: Speech Recognition System Robust against Optimized Audio Adversarial Examples.

Bibliographic Details
Title: AudioGuard: Speech Recognition System Robust against Optimized Audio Adversarial Examples.
Authors: Kwon, Hyun
Source: Multimedia Tools & Applications; Jun2024, Vol. 83 Issue 20, p57943-57962, 20p
Subject Terms: Automatic speech recognition, Artificial neural networks, Intrusion detection systems (Computer security), Image recognition (Computer vision), Machine learning
Abstract: Deep neural networks provide good performance in image recognition, voice recognition, pattern recognition, and intrusion detection. However, deep neural networks are vulnerable to adversarial examples. Adversarial examples are samples that are created by adding a small amount of noise to normal data in such a way that they are recognized as normal by humans but are misclassified by a target model. In this paper, we propose a method for defending against audio adversarial examples using a noise vector, without the need for a separate module or process. The proposed method correctly identifies adversarial examples while maintaining the model's accuracy on normal samples by using a noise vector. In our experiments, the Mozilla Common Voice dataset was used as test data, with TensorFlow as the machine learning library. The experimental results showed that the proposed method correctly identified the adversarial examples with 84.2% accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  – Url: https://dx.doi.org/doi:10.1007/s11042-023-15961-2
    Name: EDS - Springer Nature Journals (s7799221)
    Category: fullText
    Text: View record at Springer
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DbLabel: Complementary Index
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PubType: Academic Journal
PubTypeId: academicJournal
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IllustrationInfo
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  Data: AudioGuard: Speech Recognition System Robust against Optimized Audio Adversarial Examples.
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  Data: <searchLink fieldCode="AR" term="%22Kwon%2C+Hyun%22">Kwon, Hyun</searchLink>
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  Data: Multimedia Tools & Applications; Jun2024, Vol. 83 Issue 20, p57943-57962, 20p
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  Data: <searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Deep neural networks provide good performance in image recognition, voice recognition, pattern recognition, and intrusion detection. However, deep neural networks are vulnerable to adversarial examples. Adversarial examples are samples that are created by adding a small amount of noise to normal data in such a way that they are recognized as normal by humans but are misclassified by a target model. In this paper, we propose a method for defending against audio adversarial examples using a noise vector, without the need for a separate module or process. The proposed method correctly identifies adversarial examples while maintaining the model's accuracy on normal samples by using a noise vector. In our experiments, the Mozilla Common Voice dataset was used as test data, with TensorFlow as the machine learning library. The experimental results showed that the proposed method correctly identified the adversarial examples with 84.2% accuracy. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-023-15961-2
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      – SubjectFull: Intrusion detection systems (Computer security)
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      – SubjectFull: Image recognition (Computer vision)
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              Text: Jun2024
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              Y: 2024
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