2020/04/19 by Rohan Kumar Das, Xiaohai Tian, Das, Rohan Kumar +5 · 3 citations
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Audio and Speech Processing (eess.AS) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Electrical engineering #Network Security and Intrusion Detection #Speech Recognition and Synthesis #cs.CR #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.08849
5 pages, 1 figure, Submitted to Interspeech 2020
arxiv created 2020/04/19 · openalex publication_date 2020/04/19 · arxiv updated 2020/04/21 · openalex created_date 2020/04/24 · openalex updated_date 2026/07/28
Security of automatic speaker verification (ASV) systems is compromised by various spoofing attacks. While many types of non-proactive attacks (and their defenses) have been studied in the past, attacker's perspective on ASV, represents a far less explored direction. It can potentially help to identify the weakest parts of ASV systems and be used to develop attacker-aware systems. We present an overview on this emerging research area by focusing on potential threats of adversarial attacks on ASV, spoofing countermeasures, or both. We conclude the study with discussion on selected attacks and leveraging from such knowledge to improve defense mechanisms against adversarial attacks.