Adversarial Black-Box Attacks on Automatic Speech Recognition Systems Using Multi-Objective Evolutionary Optimization.

In this work, we propose a framework which uses multi-objective evolutionary optimization to perform both targeted and un-targeted black-box attacks on Automatic Speech Recognition (ASR) systems. ... Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. ... Automatic Speech Recognition (ASR) systems are becoming ubiquitous with the pervasiveness of smart devices. ...

<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.01312v2">arXiv:1811.01312v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lgbyp4nrmbav7ilpdrym2qpw5q">fatcat:lgbyp4nrmbav7ilpdrym2qpw5q</a> </span>

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In this work, we propose a framework which uses multi-objective evolutionary optimization to perform both targeted and un-targeted blackbox attacks on Automatic Speech Recognition (ASR) systems. ... Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. ... Conclusion and Future Work In this work, we introduce an algorithm agnostic framework for attacking ASR systems using evolutionary multi-objective optimization. ...

<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21437/interspeech.2019-2420">doi:10.21437/interspeech.2019-2420</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/interspeech/KhareAM19.html">dblp:conf/interspeech/KhareAM19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xag6wuxmn5a35bpugadyg6mkjq">fatcat:xag6wuxmn5a35bpugadyg6mkjq</a> </span>

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This paper proposes a black-box adversarial attack method to automatic speech recognition systems. ... The proposed method in this paper adopts Evolutionary Multi-objective Optimization (EMO)that allows it generating robust adversarial examples under black-box scenario. ... CONCLUSION This paper proposes a method to generate robust adversarial examples using evolutionary multi-objective optimization for ASR systems. ...

<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.11138v2">arXiv:2012.11138v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6ngwwhwiqbhfzieka6mpmf5gpa">fatcat:6ngwwhwiqbhfzieka6mpmf5gpa</a> </span>

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Unfortunately, recent research has shown that those systems based on deep neural networks are vulnerable to adversarial examples, which attract significant attention to VPS security. ... Then we provide a concise introduction to defense methods against adversarial attacks. ... In the attack on the black-box speech recognition system, the authors of [13] first introduced the genetic algorithm to the black-box speech recognition system. ...

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With the wide use of Automatic Speech Recognition (ASR) in applications such as human machine interaction, simultaneous interpretation, audio transcription, etc., its security protection becomes increasingly ... More importantly, we align the research in this domain with that on security in Image Recognition System (IRS), which has been extensively studied, using the domain knowledge in the latter to help understand ... For example, the authors in [54] propose evolutionary multi-objective optimization approach to attack two white-box ASR systems in both un-targeted and targeted settings. ...

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We conduct experiments on the automatic speech recognition (ASR) task with adversarial audio attacks. ... Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. ... [5] proposed a multi-objective evolutionary optimization method to craft adversarial examples on ASR systems instead of gradient-based approaches [4, 6] . ...

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However, prior research investigating the security of audio CAPTCHAs found them highly vulnerable to automated attacks using Automatic Speech Recognition (ASR) systems. ... The aaeCAPTCHA system exploits audio adversarial examples as CAPTCHAs to prevent the ASR systems from automatically solving them. ... This work is supported in part by the US NSF under grants OIA-1946231 and CNS-2117785. ...

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In particular, we focus on four types of attacks associated with security threats of deep learning: model extraction attack, model inversion attack, poisoning attack and adversarial attack. ... In order to unveil the security weaknesses and aid in the development of a robust deep learning system, we undertake an investigation on attacks towards deep learning, and analyze these attacks to conclude ... [158] undertook a poisoning attack towards multi-class problem based on back-gradient optimization. ...

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Therefore, attacks and defenses on adversarial examples draw great attention. ... Under the taxonomy, applications for adversarial examples are investigated. We further elaborate on countermeasures for adversarial examples. ... Attackers can generate adversarial commands against automatic speech recognition (ASR) models and Voice Controllable System (VCS) [23] , [24] such as Apple Siri [25] , Amazon Alexa [26] , and Microsoft ...

<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnnls.2018.2886017">doi:10.1109/tnnls.2018.2886017</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30640631">pmid:30640631</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/enznysw3svfzdjrmubwkedr6me">fatcat:enznysw3svfzdjrmubwkedr6me</a> </span>

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Therefore, attacks and defenses on adversarial examples draw great attention. ... In this paper, we review recent findings on adversarial examples for deep neural networks, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods. ... Adversarial commands can be generated by attackers against automatic speech recognition (ASR) models and Voice Controllable System (VCS) [23] , [24] such as Apple Siri [25] , Amazon Alexa [26] , and ...

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for Ensemble Optimization Based on Developmental Genetic Programming Gabriela Suchoparova and Roman Neruda .......... 631 Adversarial Audio Attacks that Evade Temporal Dependency Heng Liu and Gregory ... Li and Dongbin Zhao .......... 1478 Edge Computing Based Smart Aquaponics Monitoring System Using Deep Learning in IoT Environment Applicability issues of Evasion-Based Adversarial Attacks and Mitigation ...

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In this work, we introduce the reader to the basics of multi- objective hyperparameter optimization and motivate its usefulness in applied ML. ... , resulting in a multi-objective optimization problem. ... Black box optimization As there is generally no analytical expression of the general hyperparameter optimization problem, it forms a black-box function. ...

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In [2], we reviewed the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses) until the advent of year 2018. ... However, it is now known that DL is vulnerable to adversarial attacks that can manipulate its predictions by introducing visually imperceptible perturbations in images and videos. ... [241] used transferability to fool face recognition systems in another black-box scenario. ...

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Security and Privacy Issues in Deep Learning [article]

Ho Bae, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha, Hyungyu Lee, Sungroh Yoon

<span title="2021-03-10">2021</span> <i > arXiv </i>   <span class="release-stage" >pre-print</span>

Poisoning attacks compromise the training process by corrupting the data with malicious examples, while evasion attacks use adversarial examples to disrupt entire classification process. ... Security attacks can be divided based on when they occur: if an attack occurs during training, it is known as a poisoning attack, and if it occurs during inference (after training) it is termed an evasion ... In addition to the medical domain, proposed the first adversarial examples of automatic speech recognition. ...

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Second, we compare DAEGEN with state-of-the-art black-box adversarial attack methods (simba and tremba), by adapting them to work on a differential setting. ... Algorithmically, DAEGEN uses a local search-based optimization algorithm to find DIAEs by iteratively perturbing an input to maximize the difference of two models on predicting the input. ... Both works perform targeted attacks (while DAEGEN only performs non-targeted attacks) and formulate their optimization problem as a multi-objective optimization problem, where they try to maximize the ...

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Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization [article]

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Adversarial Black-Box Attacks on Automatic Speech Recognition Systems Using Multi-Objective Evolutionary Optimization

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Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition [article]

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Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview

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SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems [article]

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Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement [article]

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aaeCAPTCHA: The Design and Implementation of Audio Adversarial CAPTCHA [article]

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Towards Security Threats of Deep Learning Systems: A Survey [article]

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Adversarial Examples: Attacks and Defenses for Deep Learning

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Adversarial Examples: Attacks and Defenses for Deep Learning [article]

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Table of Contents

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Multi-Objective Hyperparameter Optimization – An Overview [article]

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Advances in adversarial attacks and defenses in computer vision: A survey [article]

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Security and Privacy Issues in Deep Learning [article]

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Generating Adversarial Inputs Using A Black-box Differential Technique [article]

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