"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 21 of its 91 references that are in the map. First posted on arXiv 20 Sep 2021.
Builds on
21 of 91 references.
References · 21
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
- 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
- 2017Generalized End-to-End Loss for Speaker Verificationcited by 290
- 2018Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesiscited by 280
- 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 189
- 2019Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognitioncited by 123
- 2018Neural Voice Cloning with a Few Samplescited by 100
- 2018Hierarchical Generative Modeling for Controllable Speech Synthesiscited by 93
- 2020Unsupervised Speech Decomposition via Triple Information Bottleneckcited by 87
- 2018StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networkscited by 78
- 2019Who is Real Bob? Adversarial Attacks on Speaker Recognition Systemscited by 66
- 2015End-to-End Text-Dependent Speaker Verificationcited by 49
9 more
- 2018Fooling End-to-end Speaker Verification by Adversarial Examplescited by 39
- 2020VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecturecited by 34
- 2019Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversioncited by 29
- 2020DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voicescited by 24
- 2020Defending Your Voice: Adversarial Attack on Voice Conversioncited by 23
- 2020Speech Synthesis using EEGcited by 8
- 2020ConVoice: Real-Time Zero-Shot Voice Style Transfer with Convolutional Networkcited by 6
- 2019Voice Mimicry Attacks Assisted by Automatic Speaker Verificationcited by 4
- 2013An audio CAPTCHA to distinguish humans from computerscited by 3
Built on by
Citing papers per year: 2022: 5; 2023: 3; 2024: 2; 2025: 2.
12 papers.
- 2022Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognitioncited by 22
- 2023SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systemscited by 14
- 2023QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systemscited by 9
- 2024CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learningcited by 9
- 2022A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challengescited by 7
- 2023PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injectioncited by 5
- 2022Tubes Among Us: Analog Attack on Automatic Speaker Identificationcited by 3
- 2022Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perceptioncited by 3
- 2025XAttnMark: Learning Robust Audio Watermarking with Cross-Attentioncited by 3
- 2025De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attackscited by 2
- 2024PyGen: A Collaborative Human-AI Approach to Python Package Creationcited by 1
- 2022Deep Fake Detection, Deterrence and Response: Challenges and Opportunitiescited by 0
- 2025De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attacks3 Jul 2025
- 2025XAttnMark: Learning Robust Audio Watermarking with Cross-Attention6 Feb 2025
- 2024PyGen: A Collaborative Human-AI Approach to Python Package Creation13 Nov 2024
- 2024CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning24 Apr 2024
- 2023SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems14 Sep 2023
- 2023PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection13 Sep 2023
- 2023QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systems23 May 2023
- 2022Deep Fake Detection, Deterrence and Response: Challenges and Opportunities26 Nov 2022
- 2022Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception26 Jul 2022
- 2022Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition7 Jun 2022
- 2022A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges8 May 2022
- 2022Tubes Among Us: Analog Attack on Automatic Speaker Identification6 Feb 2022