"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World
Emily Wenger, Max Bronckers, Christian Cianfarani, Jenna Cryan, Angela Sha, Haitao Zheng, Ben Y. Zhao
摘要
Advances in deep learning have introduced a new wave of voice synthesis tools, capable of producing audio that sounds as if spoken by a target speaker. If successful, such tools in the wrong hands will enable a range of powerful attacks against both humans and software systems (aka machines). This paper documents efforts and findings from a comprehensive experimental study on the impact of deep-learning based speech synthesis attacks on both human listeners and machines such as speaker recognition and voice-signin systems. We find that both humans and machines can be reliably fooled by synthetic speech, and that existing defenses against synthesized speech fall short. These findings highlight the need to raise awareness and develop new protections against synthetic speech for both humans and machines.
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引用它的顶会 Paper17
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- Can I Hear Your Face? Pervasive Attack on Voice Authentication Systems with a Single Face ImageNan Jiang, Bangjie Sun, Terence Sim, Jun HanUSENIX Security 2024 · 被引用 7 次
它引用的顶会 Paper7
- 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
- Unsupervised Speech Decomposition via Triple Information BottleneckKaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson 等ICML 2020 · 被引用 210 次
- DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake VoicesRun Wang, Felix Juefei-Xu, Yihao Huang, Qing Guo 等ACM MM 2020 · 被引用 124 次
- The Catcher in the Field: A Fieldprint based Spoofing Detection for Text-Independent Speaker VerificationChen Yan, Yan Long, Xiaoyu Ji, Wenyuan XuCCS 2019 · 被引用 62 次
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