and targeted adversarial examples for automatic speech recognition

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Imperceptible Robust and Targeted Adversarial Examples for

We build on a long line of work studying the robustness of neural networks This research area largely began with (Biggio et al 2013; Szegedy et al 2013) who first studied adversarial examples for deep neural networks This paper focuses on adversarial examples on automatic speech recognition systems

PDF	Imperceptible Robust and Targeted Adversarial Examples for %20Robust

Is automatic speech recognition vulnerable to adversarial examples?
Automatic speech recognition (ASR) is an essential technology used in commercial products nowadays. However, the underlying deep learning models used in ASR systems are vulnerable to adversarial examples (AEs), which are generated by applying small or imperceptible perturbations to audio to fool these models.
What are adversarial examples?
1. Introduction Adversarial examples (Szegedy et al., 2013) are inputs that have been specifically designed by an adversary to cause a machine learning algorithm to produce a misclassification (Biggio et al., 2013). Initial work on adversarial examples focused mainly on the domain of image classification.
How to construct imperceptible and robust adversarial examples?
To construct imperceptible as well as robust adversarial examples, we begin with the robust adversarial examples generated in Section. D.2. In the first stage, we focus on reducing the imperceptibility by setting the initial to be 0.01 and the learning rate is set to be 1. We update the adver-sarial perturbation for 4000 iterations.
What happens if an adversarial example successfully attacks the ASR system?
If the adversarial example successfully attacks the ASR system in 4 out of 10 randomly chosen rooms, then with be increased by 2. Otherwise, for every 50 iterations, will be decreased by 0.5. In the second stage, we focus on improving the less percep-tible adversarial examples to be more robust.

PDF	Imperio: Robust Over-the-Air Adversarial Examples for Automatic ABSTRACT. Automatic speech recognition (ASR) systems can be fooled via targeted adversarial examples which induce the ASR to produce.

PDF	Imperceptible Robust and Targeted Adversarial Examples for 12 juin 2019 Imperceptible Robust and Targeted. Adversarial Examples for Automatic Speech Recognition. Yao Qin

PDF	Noise Flooding for Detecting Audio Adversarial Examples Against 25 déc. 2018 Examples Against Automatic Speech Recognition. Krishan Rajaratnam ... A graphic depicting a targeted adversarial attack from “yes” (the.

PDF	Adversarial Attacks Against Automatic Speech Recognition Systems explore adversarial examples against ASR systems [54]. They showed how an input signal (i.e. audio file) can be modified to fit the target transcription by

PDF	SYNTHESISING AUDIO ADVERSARIAL EXAMPLES FOR Adversarial examples in automatic speech recognition (ASR) are naturally model to predict incorrect or even targeted transcriptions. ASR Model.

PDF	Audio Adversarial Examples: Targeted Attacks on Speech-to-Text 30 mars 2018 In automatic speech recognition a neural network is given an audio waveform x and perform the speech-to-text transform that gives the ...

PDF	Inaudible Adversarial Perturbations for Targeted Attack in Speaker 22 mai 2020 There has been plenty of work focused on attacking automatic speech recognition (ASR) systems using adversarial examples. In [9]. Carlini et al ...

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