Skip to main content
SpringerLink
Log in

Encrypted speech authentication and recovery scheme based on fragile watermarking

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

In order to solve the problems that most of the existing speech authentication algorithms can only realize tamper localization of frames, and need to the additional recovery information during tamper recovery, an encrypted speech authentication and recovery scheme based on fragile watermarking was proposed by using least significant bit method (LSB) and linear interpolation method. Firstly, a scrambling encryption algorithm based on Henon mapping is designed to encrypt and decrypt speech. Secondly, the frame number of ciphertext speech is used to construct fragile watermarking information. Finally, the LSB method is used to embed the watermarking into the fourth place after the decimal point of encrypted speech for authentication, and residial-based linear interpolation method is used to tamper recover the unauthenticated speech. The experimental results show that the proposed scheme can realize the tamper detection and location of sampling points, and the restored tampered content has good auditory quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Bevinamarad, P. R., & Shirldonkar, M. S. (2020). Audio forgery detection techniques: present and past review. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 613–618). IEEE.https://doi.org/10.1109/ICOEI48184.2020.9143014

  2. Shi, C. H., Li, X. J., & Wang, H. X. (2020). A novel integrity authentication algorithm based on perceptual speech hash and learned dictionaries. IEEE Access, 8, 22249–22265. https://doi.org/10.1109/access.2020.2970093

    Article  Google Scholar 

  3. Qian, Q., Wang, H. X., Shi, C. H., & Wang, H. (2017). An efficient content authentication scheme in encrypted speech based on integer wavelet transform. In IEEE 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) (pp. 1–8). IEEE. https://doi.org/10.1109/APSIPA.2016.7820814

  4. Hu, H. T., & Lee, T. T. (2019). Hybrid blind audio watermarking for proprietary protection, tamper proofing, and self-recovery. IEEE Access, 7, 180395–180408. https://doi.org/10.1109/access.2019.2958095

    Article  Google Scholar 

  5. Meng, X. B., Li, C., & Tian, L. H. (2019). Detecting audio splicing forgery algorithm based on local noise level estimation. In 2018 5th International Conference on Systems and Informatics (ICSAI) (pp. 861–865). IEEE.https://doi.org/10.1109/ICSAI.2018.8599318

  6. Wang, S. B., Yuan, W. T., Wang, J. M., & Unoki, M. (2019). Detection of speech tampering using sparse representations and spectral manipulations based information hiding. Speech Communication, 112, 1–14. https://doi.org/10.1016/j.specom.2019.06.004

    Article  Google Scholar 

  7. Wang, Z., Wang, J., Zeng, C., Min, Q., Tian, Y., & Zuo, M. (2018). Digital audio tampering detection based on ENF consistency. In 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) (pp. 209–214). IEEE. https://doi.org/10.1109/ICWAPR.2018.8521378

  8. Li, C., Sun, Y., Meng, X., & Tian, L. (2019). Homologous audio copy-move tampering detection method based on pitch. In 2019 IEEE 19th International Conference on Communication Technology (ICCT) (pp. 530–534). IEEE.https://doi.org/10.1109/ICCT46805.2019.8947002

  9. Yan, Q., Yang, R., & Huang, J. (2019). Robust copy–move detection of speech recording using similarities of pitch and formant. IEEE Transactions on Information Forensics and Security, 14(9), 2331–2341. https://doi.org/10.1109/TIFS.2019.2895965

    Article  Google Scholar 

  10. Esmaeilbeig, Z., & Ghaemmaghami, S. (2018). Compressed video watermarking for authentication and reconstruction of the audio part. In 2018 15th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC) (pp. 1–6). ISC. https://doi.org/10.1109/ISCISC.2018.8546897

  11. Liu, Z., Huang, J., & Sun, X. (2017). A security watermark scheme used for digital speech forensics. Multimedia Tools and Applications, 76, 9297–9317. https://doi.org/10.1007/s11042-016-3533-9

    Article  Google Scholar 

  12. Wang, J., & He, J. (2017). A speech content authentication algorithm based on a novel watermarking method. Multimedia Tools and Applications, 76, 14799–14814. https://doi.org/10.1007/s11042-016-4027-5

    Article  Google Scholar 

  13. Wang, J., Liu, Z., He, J., & Qi, C. (2015). An authentication and recovery scheme for digital speech signal based on DWT. In 2015 International Workshop on Digital Watermarking (Vol. 9569, pp. 206–219). Springer. https://doi.org/10.1007/978-3-319-31960-5_17

  14. Qian, Q., Wang, H., Sun, X., et al. (2018). Speech authentication and content recovery scheme for security communication and storage. Telecommunication Systems, 67, 635–649. https://doi.org/10.1007/s11235-017-0360-x

    Article  Google Scholar 

  15. Menendez-Ortiz, A., Feregrino-Uribe, C., Garcia-Hernandez, J., et al. (2017). Self-recovery scheme for audio restoration after a content replacement attack. Multimedia Tools and Applications, 76(12), 14197–14224. https://doi.org/10.1007/s11042-016-3783-6

    Article  Google Scholar 

  16. Menendez-Ortiz, A., Feregrino-Uribe, C., & Garcia-Hernandez, J. (2018). Self-recovery scheme for audio restoration using auditory masking. PLoS ONE, 13(9), e0204442. https://doi.org/10.1371/journal.pone.0204442

    Article  Google Scholar 

  17. Brajović, M., Stanković, I., Daković, M., & Stanković, L. (2021). The DCT domain sparsity-assisted detection and recovery of impulsively disturbed samples. Multimedia Tools and Applications, 80(4), 6221–6234. https://doi.org/10.1007/s11042-020-09998-w

    Article  Google Scholar 

  18. Lu, W., Chen, Z., Li, L., et al. (2018). Watermarking based on compressive sensing for digital speech detection and recovery. Sensors, 18(7), 2390. https://doi.org/10.3390/s18072390

    Article  Google Scholar 

  19. Liu, Z. H., Sun, F., & Qi, C. D. (2018). An authentication and recovery algorithm based on DWT. Journal of the China Railway Society, 40(3), 76–81. https://doi.org/10.3969/j.issn1001-8360.2018.03.011 in Chinese.

    Article  Google Scholar 

  20. Liu, Z. H., Luo, D., Huang, J. W., et al. (2017). Tamper recovery algorithm for digital speech signal based on DWT and DCT. Multimedia Tools and Applications, 76(10), 12481–12504. https://doi.org/10.1007/s11042-016-3664-z

    Article  Google Scholar 

  21. Qian, Q., Wang, H. X., Abdullahi, S. M., Wang, H., & Shi, C. H. (2016). Speech authentication and recovery scheme in encrypted domain. In 2016 International Workshop on Digital Watermarking (Vol. 10082, pp. 46–60). Springer. https://doi.org/10.1007/978-3-319-53465-7_4

  22. Fan, M. Q. (2020). A source coding scheme for authenticating audio signal with capability of self-recovery and anti-synchronization counterfeiting attack. Multimedia Tools and Applications, 79(1–2), 1037–1055. https://doi.org/10.1007/s11042-019-08095-x

    Article  Google Scholar 

  23. Gomez-Ricardez, J. J., & Garcia-Hernandez, J. J. (2019). An audio self-recovery scheme that is robust to discordant size content replacement attack. In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 825–828). IEEE. https://doi.org/10.1109/mwscas.2018.8623934

  24. Ma, Y., Hong, H., Zhao, H., Li, H., Gu, C., & Zhu, X. (2019). Speech recovery based on auditory radar and webcam. In 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) (pp. 1–3). IEEE. https://doi.org/10.1109/imbioc.2019.8777840

  25. Fakhar Firouzeh, F., Chinneck, J. W., & Rajan, S. (2020). Maximum feasible subsystem algorithms for recovery of compressively sensed speech. IEEE Access, 8, 82539–82550. https://doi.org/10.1109/access.2020.2990155

    Article  Google Scholar 

  26. Al-hazaimeh, O. M. (2021). A new speech encryption algorithm based on dual shuffling Hénon chaotic map. International Journal of Electrical and Computer Engineering (IJECE), 11(3), 2203–2210. https://doi.org/10.11591/ijece.v11i3.pp2203-2210

    Article  Google Scholar 

  27. Wang, D., Zhang, X. W. (2015). Thchs-30: A free Chinese speech corpus. arXiv preprint arXiv:1512.01882

  28. Zhang, Q., Zhou, L., Zhang, T., et al. (2019). A retrieval algorithm of encrypted speech based on short-term cross-correlation and perceptual hashing. Multimed. Tools Appl., 78(13), 17825–17846. https://doi.org/10.1007/s11042-019-7180-9

    Article  Google Scholar 

  29. Zhang, Q., Li, Y., & Hu, Y. (2021). A retrieval algorithm for encrypted speech based on convolutional neural network and deep hashing. Multimedia Tools and Applications, 80(3), 1201–1221. https://doi.org/10.1007/s11042-020-09748-y

    Article  Google Scholar 

  30. Zhang, Q., Zhang, D., & Xu, F. (2021). An encrypted speech authentication and tampering recovery method based on perceptual hashing. Multimedia Tools and Applications, 80(23), 24925–24948. https://doi.org/10.1007/s11042-021-10905-0

    Article  Google Scholar 

Download references

Acknowledgements

The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

Funding

This work is supported by the National Natural Science Foundation of China (Nos. 61862041, 61363078).

Author information

Authors and Affiliations

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, People’s Republic of China

    Qiu-yu Zhang & Fu-jiu Xu

Authors
  1. Qiu-yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fu-jiu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiu-yu Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Qy., Xu, Fj. Encrypted speech authentication and recovery scheme based on fragile watermarking. Telecommun Syst 82, 125–140 (2023). https://doi.org/10.1007/s11235-022-00976-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-022-00976-1

Keywords

Search

Navigation