Personal Voice Assistant Security and Privacy—A Survey

Cheng, Peng; Roedig, Utz

doi:10.1109/jproc.2022.3153167

Cited by 32 publications

(13 citation statements)

References 85 publications

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“…They come in many shapes and forms, and the most known PVAs are Siri, Alexa, Google Home, and Amazon Echo. These personal assistants can be integrated within our smartphones and tablets and can be attached to different home devices and smart cars (Mercedes and Jaguar) or might operate on their own [1]. The use of PVAs increased significantly over the last decade; over 21% Americans own a smart speaker, and 80% of the American population own a smartphone [13].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The cloud will run Automatic Speech Recognition (ASR), which analyzes the speech and scans it to identify commands. Finally, the cloud will carry out the requested commands and send back a response to the device, which is then played for the user through speakers [1]. ASR is a research field combining computer science, linguistics, and electrical engineering.…”

Section: Personal Voice Assistance Pvamentioning

confidence: 99%

“…In the last few years, many research works have been dedicated to performing or activating tasks using voice commands [1]. Those voice commands should first be interpreted using speech recognition.…”

Section: Introductionmentioning

confidence: 99%

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LimitAccess: on-device TinyML based robust speech recognition and age classification

Maayah

Abunada

Al-Janahi

et al. 2023

Discov Artif Intell

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Automakers from Honda to Lamborghini are incorporating voice interaction technology into their vehicles to improve the user experience and offer value-added services. Speech recognition systems are a key component of smart cars, enhancing convenience and safety for drivers and passengers. In the future, safety-critical features may rely on speech recognition, but this raises concerns about children accessing such services. To address this issue, the LimitAccess system is proposed, which uses TinyML for age classification and helps parents limit children’s access to critical speech recognition services. This study employs a lite convolutional neural network (CNN) model for two different reasons: First, CNN showed superior accuracy compared to other audio classification models for age classification problems. Second, the lite model will be integrated into a microcontroller to meet its limited resource requirements. To train and evaluate our model, we created a dataset that included child and adult voices of the keyword “open”. The system approach categorizes voices into age groups (child, adult) and then utilizes that categorization to grant access to a car. The robustness of the model was enhanced by adding a new class (recordings) to the dataset, which enabled our system to detect replay and synthetic voice attacks. If an adult voice is detected, access to start the car will be granted. However, if a child’s voice or a recording is detected, the system will display a warning message that educates the child about the dangers and consequences of the improper use of a car. Arduino Nano 33 BLE sensing was our embedded device of choice for integrating our trained, optimized model. Our system achieved an overall F1 score of 87.7% and 85.89% accuracy. LimitAccess detected replay and synthetic voice attacks with an 88% F1 score.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Personal Voice Assistance Pvamentioning

confidence: 99%

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LimitAccess: on-device TinyML based robust speech recognition and age classification

Maayah

Abunada

Al-Janahi

et al. 2023

Discov Artif Intell

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“…Physiological signals are continuous time series, which are quite different from images. There are relatively few adversarial attack studies on time series [75][76][77][78], and even fewer on physiological signals. A summary of them is shown in Table 3 [4, 34-36, 67, 76, 79-88].…”

Section: Adversarial Attacks In Physiological Computingmentioning

confidence: 99%

Adversarial attacks and defenses in physiological computing: a systematic review

Fang

Zhang³

et al. 2022

NSO

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Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them. This study fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure.

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“…Personal voice assistants are increasingly used as interfaces to digital environments [1]. Voice assistants of mobile phones are listening to the specific commands in speech all the time to be wakened up.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Examples Protect Your Privacy on Speech Enhancement System

Dong¹,

Yan²,

Wang³

2023

Computer Systems Science and Engineering

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Speech is easily leaked imperceptibly. When people use their phones, the personal voice assistant is constantly listening and waiting to be activated. Private content in speech may be maliciously extracted through automatic speech recognition (ASR) technology by some applications on phone devices. To guarantee that the recognized speech content is accurate, speech enhancement technology is used to denoise the input speech. Speech enhancement technology has developed rapidly along with deep neural networks (DNNs), but adversarial examples can cause DNNs to fail. Considering that the vulnerability of DNN can be used to protect the privacy in speech. In this work, we propose an adversarial method to degrade speech enhancement systems, which can prevent the malicious extraction of private information in speech. Experimental results show that the generated enhanced adversarial examples can be removed most content of the target speech or replaced with target speech content by speech enhancement. The word error rate (WER) between the enhanced original example and enhanced adversarial example recognition result can reach 89.0%. WER of target attack between enhanced adversarial example and target example is low at 33.75%. The adversarial perturbation in the adversarial example can bring much more change than itself. The rate of difference between two enhanced examples and adversarial perturbation can reach more than 1.4430. Meanwhile, the transferability between different speech enhancement models is also investigated. The low transferability of the method can be used to ensure the content in the adversarial example is not damaged, the useful information can be extracted by the friendly ASR. This work can prevent the malicious extraction of speech.

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Personal Voice Assistant Security and Privacy—A Survey

Cited by 32 publications

References 85 publications

LimitAccess: on-device TinyML based robust speech recognition and age classification

LimitAccess: on-device TinyML based robust speech recognition and age classification

Adversarial attacks and defenses in physiological computing: a systematic review

Adversarial Examples Protect Your Privacy on Speech Enhancement System

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