62 research outputs found
Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech in Real Time
Automatic speech recognition (ASR) systems have been shown to be vulnerable
to adversarial examples (AEs). Recent success all assumes that users will not
notice or disrupt the attack process despite the existence of music/noise-like
sounds and spontaneous responses from voice assistants. Nonetheless, in
practical user-present scenarios, user awareness may nullify existing attack
attempts that launch unexpected sounds or ASR usage. In this paper, we seek to
bridge the gap in existing research and extend the attack to user-present
scenarios. We propose VRIFLE, an inaudible adversarial perturbation (IAP)
attack via ultrasound delivery that can manipulate ASRs as a user speaks. The
inherent differences between audible sounds and ultrasounds make IAP delivery
face unprecedented challenges such as distortion, noise, and instability. In
this regard, we design a novel ultrasonic transformation model to enhance the
crafted perturbation to be physically effective and even survive long-distance
delivery. We further enable VRIFLE's robustness by adopting a series of
augmentation on user and real-world variations during the generation process.
In this way, VRIFLE features an effective real-time manipulation of the ASR
output from different distances and under any speech of users, with an
alter-and-mute strategy that suppresses the impact of user disruption. Our
extensive experiments in both digital and physical worlds verify VRIFLE's
effectiveness under various configurations, robustness against six kinds of
defenses, and universality in a targeted manner. We also show that VRIFLE can
be delivered with a portable attack device and even everyday-life loudspeakers.Comment: Accepted by NDSS Symposium 202
Security and privacy problems in voice assistant applications: A survey
Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain
Security and Privacy Problems in Voice Assistant Applications: A Survey
Voice assistant applications have become omniscient nowadays. Two models that
provide the two most important functions for real-life applications (i.e.,
Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR)
models and Speaker Identification (SI) models. According to recent studies,
security and privacy threats have also emerged with the rapid development of
the Internet of Things (IoT). The security issues researched include attack
techniques toward machine learning models and other hardware components widely
used in voice assistant applications. The privacy issues include technical-wise
information stealing and policy-wise privacy breaches. The voice assistant
application takes a steadily growing market share every year, but their privacy
and security issues never stopped causing huge economic losses and endangering
users' personal sensitive information. Thus, it is important to have a
comprehensive survey to outline the categorization of the current research
regarding the security and privacy problems of voice assistant applications.
This paper concludes and assesses five kinds of security attacks and three
types of privacy threats in the papers published in the top-tier conferences of
cyber security and voice domain.Comment: 5 figure
Privacy-preserving and Privacy-attacking Approaches for Speech and Audio -- A Survey
In contemporary society, voice-controlled devices, such as smartphones and
home assistants, have become pervasive due to their advanced capabilities and
functionality. The always-on nature of their microphones offers users the
convenience of readily accessing these devices. However, recent research and
events have revealed that such voice-controlled devices are prone to various
forms of malicious attacks, hence making it a growing concern for both users
and researchers to safeguard against such attacks. Despite the numerous studies
that have investigated adversarial attacks and privacy preservation for images,
a conclusive study of this nature has not been conducted for the audio domain.
Therefore, this paper aims to examine existing approaches for
privacy-preserving and privacy-attacking strategies for audio and speech. To
achieve this goal, we classify the attack and defense scenarios into several
categories and provide detailed analysis of each approach. We also interpret
the dissimilarities between the various approaches, highlight their
contributions, and examine their limitations. Our investigation reveals that
voice-controlled devices based on neural networks are inherently susceptible to
specific types of attacks. Although it is possible to enhance the robustness of
such models to certain forms of attack, more sophisticated approaches are
required to comprehensively safeguard user privacy
Acoustic-channel attack and defence methods for personal voice assistants
Personal Voice Assistants (PVAs) are increasingly used as interface to digital environments. Voice commands are used to interact with phones, smart homes or cars. In the US alone the number of smart speakers such as Amazon’s Echo and Google Home has grown by 78% to 118.5 million and 21% of the US population own at least one device. Given the increasing dependency of society on PVAs, security and privacy of these has become a major concern of users, manufacturers and policy makers. Consequently, a steep increase in research efforts addressing security and privacy of PVAs can be observed in recent years. While some security and privacy research applicable to the PVA domain predates their recent increase in popularity and many new research strands have emerged, there lacks research dedicated to PVA security and privacy. The most important interaction interface between users and a PVA is the acoustic channel and acoustic channel related security and privacy studies are desirable and required. The aim of the work presented in this thesis is to enhance the cognition of security and privacy issues of PVA usage related to the acoustic channel, to propose principles and solutions to key usage scenarios to mitigate potential security threats, and to present a novel type of dangerous attack which can be launched only by using a PVA alone. The five core contributions of this thesis are: (i) a taxonomy is built for the research domain of PVA security and privacy issues related to acoustic channel. An extensive research overview on the state of the art is provided, describing a comprehensive research map for PVA security and privacy. It is also shown in this taxonomy where the contributions of this thesis lie; (ii) Work has emerged aiming to generate adversarial audio inputs which sound harmless to humans but can trick a PVA to recognise harmful commands. The majority of work has been focused on the attack side, but there rarely exists work on how to defend against this type of attack. A defence method against white-box adversarial commands is proposed and implemented as a prototype. It is shown that a defence Automatic Speech Recognition (ASR) can work in parallel with the PVA’s main one, and adversarial audio input is detected if the difference in the speech decoding results between both ASR surpasses a threshold. It is demonstrated that an ASR that differs in architecture and/or training data from the the PVA’s main ASR is usable as protection ASR; (iii) PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. A user has limited control over this process when a PVA is triggered without user’s intent or a PVA belongs to others. A user is unable to control the recording behaviour of surrounding PVAs, unable to signal privacy requirements and unable to track conversation recordings. An acoustic tagging solution is proposed aiming to embed additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal when PVA activity is assumed. Any active PVA will embed this tag into their recorded audio stream. The tag may signal a cooperating PVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and where a recording was taken if necessary. A prototype tagging device based on PocketSphinx is implemented. Using Google Home Mini as the PVA, it is demonstrated that the device can tag conversations and the tagging signal can be retrieved from conversations stored in the Google back-end system; (iv) Acoustic tagging provides users the capability to signal their permission to the back-end PVA service, and another solution inspired by Denial of Service (DoS) is proposed as well for protecting user privacy. Although PVAs are very helpful, they are also continuously monitoring conversations. When a PVA detects a wake word, the immediately following conversation is recorded and transported to a cloud system for further analysis. An active protection mechanism is proposed: reactive jamming. A Protection Jamming Device (PJD) is employed to observe conversations. Upon detection of a PVA wake word the PJD emits an acoustic jamming signal. The PJD must detect the wake word faster than the PVA such that the jamming signal still prevents wake word detection by the PVA. An evaluation of the effectiveness of different jamming signals and overlap between wake words and the jamming signals is carried out. 100% jamming success can be achieved with an overlap of at least 60% with a negligible false positive rate; (v) Acoustic components (speakers and microphones) on a PVA can potentially be re-purposed to achieve acoustic sensing. This has great security and privacy implication due to the key role of PVAs in digital environments. The first active acoustic side-channel attack is proposed. Speakers are used to emit human inaudible acoustic signals and the echo is recorded via microphones, turning the acoustic system of a smartphone into a sonar system. The echo signal can be used to profile user interaction with the device. For example, a victim’s finger movement can be monitored to steal Android unlock patterns. The number of candidate unlock patterns that an attacker must try to authenticate herself to a Samsung S4 phone can be reduced by up to 70% using this novel unnoticeable acoustic side-channel
- …