Collaborative Research: SaTC: CORE: Small: Securing IoT and Edge Devices under Audio Adversarial Attacks

协作研究：SaTC：核心：小型：在音频对抗攻击下保护物联网和边缘设备

• 批准号: 2114220
• 负责人: Yingying Chen
• 金额: $ 33万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114220&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

Powered by the advancement of artificial intelligence (AI) techniques, the next-generation voice-controllable IoT and edge systems have substantially facilitated people’s daily lives. Such systems include voice assistant systems and voice authenticated mobile banking, among many others. However, the underlying machine learning approaches used in these systems, are inherently vulnerable to audio adversarial attacks, in which an adversary can mislead the machine learning models via injecting imperceptible perturbation to the original audio input. Given the widespread adoption of voice-controllable IoT and edge systems in many privacy-critical and safety-critical applications, e.g., personal banking and autonomous driving, the in-depth understanding and investigation of severity and consequences of audio-based adversarial attack as well as the corresponding defense solutions, are highly demanded. This project will perform a comprehensive study and analysis of the vulnerability and robustness of voice-controllable IoT and edge systems against audio-domain adversarial attacks in both temporal and spatial perspectives. The research outcome of this project will form solid foundations for building trustworthy voice-controllable IoT and edge systems. The developed defense techniques will improve the security of many intelligent audio systems, such as automatic speech recognition (ASR), keyword spotting, and speaker recognition. This project will involve underrepresented students, undergraduate and graduate students, and K-12 students through a variety of engaging programs.The objective of this project is to demonstrate the feasibility of audio adversarial attacks in the physical world, determine the attack severity and consequences, and further develop defending strategies in practical environments to build attack-resilient voice-controllable Internet-of-Things (IoT) devices and edge systems. To study the possibility and severity of audio adversarial attacks in a practical time-constraint setting, the project will develop low-cost audio-agnostic synchronization-free attack launching schemes, including audio-specific fast adversarial perturbation generator and universal adversarial perturbation generator. To investigate how the adversarial perturbations survive various propagation factors in realistic environments, the project will analyze the audio distortions caused by the over-the-air propagation using an advanced room impulse response simulator and physical environment measurements. The project will also develop several defense techniques, including defensive denoiser, model enhancement, and microphone-array-based liveness detection. The presented technique will help to secure the voice-controllable IoT and edge devices under audio adversarial attacks. The project will also contribute to a new computing paradigm in audio-based adversarial machine learning in both theoretic foundations as well as safety-critical audio-oriented emerging applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在人工智能（AI）技术的发展方面，下一代语音控制的物联网和边缘系统的推动力大大支持了人们的日常生活。这样的系统包括语音助理系统和语音身份验证的移动银行业务等。但是，这些系统中使用的基础机器学习方法本质上容易受到音频对抗性攻击的影响，在这种情况下，对手可以通过向原始音频输入注入不可察觉的扰动来误导机器学习模型。鉴于在许多关键和关键安全的应用中采用了语音控制的物联网和边缘系统，例如个人银行和自动驾驶，高度要求对基于音频的对抗性攻击以及对应的国防解决方案的严重性以及对严重性的深入理解和调查。该项目将对语音控制的物联网和边缘系统的脆弱性和鲁棒性进行全面研究和分析，以针对临时和空间观点的音频域对抗攻击。该项目的研究结果将构成可靠的基础，以建立可信赖的语音控制物联网和边缘系统。开发的防御技术将提高许多智能音频系统的安全性，例如自动语音识别（ASR），关键字斑点和扬声器识别。该项目将通过各种引人入胜的计划涉及代表性不足的学生，本科生和研究生以及K-12学生。该项目的目的是证明在物理世界中音频对抗性攻击的可行性，确定攻击性的严重性和后果，并在实践环境中进一步发展攻击性的策略，以构建攻击性的语言互联网和互联网互联网（Intern Internt）。为了研究在实用的时间限制环境中音频对抗攻击的可能性和严重性，该项目将开发低成本的音频无关同步攻击启动方案，包括音频特定的快速对抗性扰动生成器和通用对抗性扰动生成器。为了研究对抗性扰动如何在现实环境中生存各种传播因素，该项目将使用高级房间脉冲响应模拟器和物理环境测量值分析由空中传播引起的音频畸变。该项目还将开发多种防御技术，包括防御性Denoiser，模型增强和基于麦克风阵列的Livices检测。提出的技术将有助于在音频对抗攻击下保护语音控制的物联网和边缘设备。该项目还将有助于基于音频的对抗机器学习的新计算范式在理论基础中以及面向安全性音频的新兴应用程序中。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛影响的评估来通过评估来通过评估来获得支持的珍贵。

项目成果

Invisible and Efficient Backdoor Attacks for Compressed Deep Neural Networks

• DOI: 10.1109/icassp43922.2022.9747582
• 发表时间: 2022-05
• 期刊: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Huy Phan;Yi Xie;Jian Liu;Yingying Chen;Bo Yuan

Robust Detection of Machine-induced Audio Attacks in Intelligent Audio Systems with Microphone Array

• DOI: 10.1145/3460120.3484755
• 发表时间: 2021-11
• 期刊: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Zhuohang Li;Cong Shi;Tianfang Zhang;Yi Xie;Jian Liu;Bo Yuan;Yingying Chen

Stealthy Backdoor Attack on RF Signal Classification

• DOI: 10.1109/icccn58024.2023.10230152
• 发表时间: 2023-07
• 期刊: 2023 32nd International Conference on Computer Communications and Networks (ICCCN)
• 作者: Tianming Zhao;Zijie Tang;Tian-Di Zhang;Huy Phan;Yan Wang;Cong Shi;Bo Yuan;Ying Chen

HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks

• DOI: 10.1609/aaai.v37i9.26244
• 发表时间: 2023-01
• 期刊: ArXiv
• 作者: Jinqi Xiao;Chengming Zhang;Yu Gong;Miao Yin;Yang Sui;Lizhi Xiang;Dingwen Tao;Bo Yuan

Universal Targeted Adversarial Attacks Against mmWave-based Human Activity Recognition

• DOI: 10.1109/infocom53939.2023.10228887
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen