Breaking Security-Critical Voice Authentication
Andre Kassis, Urs Hengartner
摘要
Voice authentication (VA) has recently become an integral part in numerous security-critical operations, such as bank transactions and call center conversations. The vulnerability of automatic speaker verification systems (ASVs) to spoofing attacks instigated the development of countermeasures (CMs), whose task is to differentiate between bonafide and spoofed speech. Together, ASVs and CMs form today’s VA systems and are being advertised as an impregnable access control mechanism. We develop the first practical attack on spoofing countermeasures, and demonstrate how a malicious actor may efficiently craft audio samples against these defenses. Previous adversarial attacks against VA have been mainly designed for the whitebox scenario, which assumes knowledge of the system’s internals, or requires large query and time budgets to launch target-specific attacks. When attacking a security-critical system, these assumptions do not hold. Our attack, on the other hand, targets common points of failure that all spoofing countermeasures share, making it real-time, model-agnostic, and completely blackbox without the need to interact with the target to craft the attack samples. The key message from our work is that CMs mistakenly learn to distinguish between spoofed and bonafide audio based on cues that are easily identifiable and forgeable. The effects of our attack are subtle enough to guarantee that these adversarial samples can still bypass the ASV as well and preserve their original textual contents. These properties combined make for a powerful attack that can bypass security-critical VA in its strictest form, yielding success rates of up to 99% with only 6 attempts. Finally, we perform the first targeted, over-telephony-network attack on CMs, bypassing several known challenges and enabling a variety of potential threats, given the increased use of voice biometrics in call centers. Our results call into question the security of modern VA systems and urge users to rethink their trust in them, in light of the real threat of attackers bypassing these measures to gain access to their most valuable resources.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper4
- SiFMimicEvader: Evading Fake Voice Detection with Adversarial Neural Mimicry AttacksXuan Hai, Xin Liu, Zihao Zhang, Ziyao Yu 等ACM MM 2025
- Open Sesame! On the Security and Memorability of Verbal PasswordsEunsoo Kim, Kiho Lee, Doowon Kim, Hyoungshick KimS&P 2025
- UnMarker: A Universal Attack on Defensive Image WatermarkingAndre Kassis, Urs HengartnerS&P 2025
- What's the Real: A Novel Design Philosophy for Robust AI-Synthesized Voice DetectionXuan Hai, Xin Liu, Yuan Tan, Gang Liu 等ACM MM 2024
它引用的顶会 Paper14
- Towards Evaluating the Robustness of Neural NetworksNicholas Carlini, David A. WagnerS&P 2017 · 被引用 9,786 次
- CommanderSong: A Systematic Approach for Practical Adversarial Voice RecognitionXuejing Yuan, Yuxuan Chen, Yue Zhao, Yunhui Long 等USENIX Security 2018 · 被引用 389 次
- Who is Real Bob? Adversarial Attacks on Speaker Recognition SystemsGuangke Chen, Sen Chen, Lingling Fan, Xiaoning Du 等S&P 2021 · 被引用 239 次
- Hearing Your Voice is Not Enough: An Articulatory Gesture Based Liveness Detection for Voice AuthenticationLinghan Zhang, Sheng Tan, Jie YangCCS 2017 · 被引用 212 次
- VoiceLive: A Phoneme Localization based Liveness Detection for Voice Authentication on SmartphonesLinghan Zhang, Sheng Tan, Jie Yang, Yingying ChenCCS 2016 · 被引用 187 次
相关 Paper
- Voiceprint Mimicry Attack Towards Speaker Verification System in Smart HomeLei Zhang, Yan Meng, Jiahao Yu, Chong Xiang 等INFOCOM 2020 · 被引用 49 次
- SiFDetectCracker: An Adversarial Attack Against Fake Voice Detection Based on Speaker-Irrelative FeaturesXuan Hai, Xin Liu, Yuan Tan, Qingguo ZhouACM MM 2023 · 被引用 6 次
- When Evil Calls: Targeted Adversarial Voice over IP NetworkHan Liu, Zhiyuan Yu, Mingming Zha, XiaoFeng Wang 等CCS 2022 · 被引用 13 次
- Tubes Among Us: Analog Attack on Automatic Speaker IdentificationShimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini 等USENIX Security 2023
- SMACK: Semantically Meaningful Adversarial Audio AttackZhiyuan Yu, Yuanhaur Chang, Ning Zhang, Chaowei XiaoUSENIX Security 2023
