Detecting Voice Cloning Attacks via Timbre Watermarking
Open accessAbstract
Nowadays, it is common to release audio content to the public, for social sharing or commercial purposes.However, with the rise of voice cloning technology, attackers have the potential to easily impersonate a specific person by utilizing his publicly released audio without any permission.Therefore, it becomes significant to detect any potential misuse of the released audio content and protect its timbre from being impersonated.To this end, we introduce a novel concept, "Timbre Watermarking", which embeds watermark information into the target individual's speech, eventually defeating the voice cloning attacks.However, there are two challenges: 1) robustness: the attacker can remove the watermark with common speech preprocessing before launching voice cloning attacks; 2) generalization: there are a variety of voice cloning approaches for the attacker to choose, making it hard to build a general defense against all of them.To address these challenges, we design an end-to-end voice cloning-resistant detection framework.The core idea of our solution is to embed the watermark into the frequency domain, which is inherently robust against common data processing methods.A repeated embedding strategy is adopted to further enhance the robustness.To acquire generalization across different voice cloning attacks, we modulate their shared process and integrate it into our framework as a distortion layer.Experiments demonstrate that the proposed timbre watermarking can defend against different voice cloning attacks, exhibit strong resistance against various adaptive attacks (e.g., reconstruction-based removal attacks, watermark overwriting attacks), and achieve practicality in real-world services such as PaddleSpeech, Voice-Cloning-App, and so-vits-svc.In addition, ablation studies are also conducted to verify the effectiveness of our design.Some audio samples are available at https://timbrewatermarking.github.io/samples.
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The method exhibits strong resistance to advanced adversarial attacks targeting watermark integrity, such as watermark overwriting and removal attacks.
“Furthermore, the method exhibits strong resistance to advanced adversarial attacks targeting watermark integrity, such as watermark overwriting and removal attacks.”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [171]
The Watermark Overwriting Attack section reports that the default method fails against an insider overwriting attack, although an enhanced strategy achieves 100% extraction accuracy. The Audio Reconstruction-based Removal Attacks section reports that a removal attempt left 74.90% extraction accuracy and produced poor-quality speech. The claim captures resistance to removal and to overwriting with the enhanced strategy, but omits the default method’s vulnerability. (LLM, conf=0.84)
Experimental — accuracy not yet measuredWatermark extraction accuracy remains at 100% against preprocessing attacks such as resampling and compression while maintaining nearly 90% accuracy under extreme conditions like low-bitrate compression and low-pass filtering.
“Experimental results show that the watermark extraction accuracy remains at 100% against preprocessing attacks such as resampling and compression while maintaining nearly 90% accuracy under extreme conditions like low-bitrate compression and low-pass filtering.”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [171]
Even when only 75% of the training data are watermarked, cloned voices can still be effectively detected.
“Even when only 75% of the training data are watermarked, cloned voices can still be effectively detected.”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [171]
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Ajitha, V., Sherly, S. I., Selvamuthukumaran, N., Kavitha, R., Ramya, M., & Krishnaraj, M. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-5265-5_39 · Cited by 1 · see citing works
Speaker Privacy and Security in the Big Data Era: Protection and Defense Against Deepfake
Chen, L., Lee, K. A., Ling, Z.-H., Wang, X., Das, R. K., Toda, T., & Li, H. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11249028 · Cited by 1 · see citing works
A robust audio watermarking method based on dual-encoder U-Net and short-time Fourier transform
Wen, S., Tan, H., Li, J., & Hu, T. (2026) · Cyber Security and Applications · doi:10.1016/j.csa.2026.100129 · Cited by 1 · see citing works
StreamMark: A Deep Learning-Based Semi-Fragile Audio Watermarking for Proactive Deepfake Detection
Liu, Z., & Cerňak, M. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463325 · Cited by 1 · see citing works
Perception-Aware Attack Against Music Copyright Detection: Impacts and Defenses
Duan, R., Qu, Z., Zhao, S., Ding, L., Liu, Y., & Lu, Z. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3522849
A hybrid voice cloning for inclusive education in low-resource environments
Younus, M. M., Iqbal, A., Durrani, E. E. N., Ahmad, N., & Ladan, M. I. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1675616
Robust Voice Cloning Classification: Evaluating Monolingual and Regional Dialect Generalization
Priyanka, M., Rushita, G., Priyanka, G., Amritha, P., Aiman, N., & Suresha, R. (2025) · International Conference on Networks, Multimedia and Information Technology (NMITCON) · doi:10.1109/nmitcon65824.2025.11188414
Protecting Your Voice: Temporal-Aware Robust Watermarking
Li, Y., Liu, W., Lin, D., Tian, H., & Wang, H. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3724105
MultiSiFer: Detecting Multiple-Speaker Fake Voice Without Speaker-Irrelative Features
Liu, X., Xuan, H., Yu, Z., Zhang, Z., Fei, Q., & Zhou, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3648834
AudioAuth: A Dual-Watermarking Framework for Robust Audio Integrity and Source Attribution
Pujari, A., & Rattani, A. (2026) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2026.3679274
Voice Watermarking for Authentication and Copyright Protection Using Neural Models
Tran, M. T. Q., Nguyen, N., Nguyen, D. M., Sung, L. J., Ling, H.-C., & Tran, L. D. (2026) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic68212.2026.11454410
Zhang, J., Wei, G., Long, Y., Wang, W., & Zuo, K. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-21168-2_15
Self-citationReinforced Temporally Sparse Gating for High-Fidelity Speech Watermarking
Fang, X., Li, J., Liu, J., & Zou, L. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3690581
Pan, J., Ye, X., Liu, B., & Deng, H. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-23453-7_23
Luo, T., Zhou, F., & Qiao, G. (2026) · The Journal of the Acoustical Society of America · doi:10.1121/10.0044106
The Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection
Müller, N. M., & Debus, P. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3724777
Gu, Y., Xu, H., Chen, J., Xu, J., Feng, Y., Jia, J., Wu, C., Li, T., Shen, J., Ma, J., et al. (2026) · Chinese Journal of Electronics · doi:10.23919/cje.2025.00.174
Su, Y., Qi, L., & Li, F. (2026) · Applied Optics · doi:10.1364/ao.606552
Liu, J. R., Dong, S., Li, H., Gao, C., Shin, K. G., Wang, H., Ren, Y., & Sun, L. (2026) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn69566.2026.00037
GAGE: Generative Adversarial Enhancement for High-Fidelity Audio Watermark Removal
Wang, W. K., Wei, G., Zhang, C. Z., Long, Y., & Li, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2859-1_6
Jin, Y., Li, Q., Liu, L., & Ni, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3018-1_12
Exploiting Neural Speech Enhancement Models to Undermine Voice Watermarking
Gomez, A. M., Martín-Doñas, J. M., & Peinado, A. M. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3718252
Zhang, Y., Ye, D., Tondi, B., & Barni, M. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3723197
Archana, Panda, J., & Kumar, S. (2026) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea69821.2026.11702134
Michael, K. O., Ikaka, L. N., Effiom, N. E., Mbeh, M. R.-O., Bassey, F. B.-I., Garki, B. L., Lawrence, O. D., & Essang, S. O. (2026) · Recent Advances in Natural Sciences · doi:10.61298/rans.2026.4.2.416
- Authors
- Liu, C., Zhang, J., Zhang, T., Yang, X., Zhang, W., & Yu, N.
- Affiliations
- University of Science and Technology of China (CN)
- Nanyang Technological University (SG)
- Venue
- Proceedings 2024 Network and Distributed System Security Symposium
- Publisher
- Internet Society
- Published
- 2024
- OpenAlex
- W4391725339
- Cited by
- 40
- References
- 63
- Citations by year
- 2026: 19 · 2025: 13 · 2024: 8
- FWCI
- 8.23
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Network and Distributed System Security Symposium (publishedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (U20B2047, 62121002, 62002334, 62072421, 62102386)
- Ministry of Education, India
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- voice spoofing detection, watermark robustness, frequency domain embedding, adaptive attacks, watermark removal attacks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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