Towards the universal defense for query-based audio adversarial attacks on speech recognition system  

作　　者：Feng Guo Zheng Sun Yuxuan Chen Lei Ju 

机构地区：[1]School of Cyber Science and Technology,Shandong University,Qingdao,China [2]Quancheng Laboratory,QCL,Jinan,China

出　　处：《Cybersecurity》2024年第1期53-70,共18页网络空间安全科学与技术（英文）

基　　金：supported in part by NSFC No.62202275,Shandong-SF No.ZR2022QF012 projects.

摘　　要：Recently,studies show that deep learning-based automatic speech recognition(ASR)systems are vulnerable to adversarial examples(AEs),which add a small amount of noise to the original audio examples.These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying ASR systems and devices.The existing defense methods are either limited in application or only defend on results,but not on process.In this work,we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process.The insight of this method is based on the observation:many existing audio AE attacks utilize query-based methods,which means the adversary must send continuous and similar queries to target ASR models during the audio AE generation process.Inspired by this observation,We propose a memory mechanism by adopting audio fingerprint technology to analyze the similarity of the current query with a certain length of memory query.Thus,we can identify when a sequence of queries appears to be suspectable to generate audio AEs.Through extensive evaluation on four state-of-the-art audio AE attacks,we demonstrate that on average our defense identify the adversary’s intent with over 90%accuracy.With careful regard for robustness evaluations,we also analyze our proposed defense and its strength to withstand two adaptive attacks.Finally,our scheme is available out-of-the-box and directly compatible with any ensemble of ASR defense models to uncover audio AE attacks effectively without model retraining.

关 键 词：Adversarial attacks DEFENSE Memory mechanism Query-based 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]