A new DCT filters-based method to improve the resistance of ternary watermarks in audio files against attacks

R.Kh. Latypov, E.L. Stolov

Kazan Federal University, Kazan, 420008 Russia

ОРИГИНАЛЬНАЯ СТАТЬЯ

Полный текст PDF
DOI: 10.26907/2541-7746.2021.1.77-89

For citation: Latypov R.Kh., Stolov E.L. A new DCT filters-based method to improve the resistance of ternary watermarks in audio files against attacks. Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, 2021, vol. 163, no. 1, pp. 77–89. doi: 10.26907/2541-7746.2021.1.77-89.

Для цитирования: Latypov R.Kh., Stolov E.L. A new DCT filters-based method to improve the resistance of ternary watermarks in audio files against attacks // Учен. зап. Казан. ун-та. Сер. Физ.-матем. науки. – 2021. – Т. 163, кн. 1. – С. 77–89. – doi: 10.26907/2541-7746.2021.1.77-89.

Abstract

Watermarks used to verify the authorship of various works of art is a simple tool available to novice authors. This paper proposes a new technique to enhance the resistance of watermarks in audio files. As parts of a melody or image, leveraged watermarks can be recognized by human beings, even if dramatically corrupted. Nevertheless, increasing the quality of extracted watermarks is always helpful. This is achieved by new methods developed for damaged watermark restoration. In our methods, the source media is converted into a ternary code. The suggested technique is based on the restoration of a part of the source’s features via its ternary code. Discrete cosine transform matrix rows are used as the final impulse response filters. Some features of the source are extracted by applying a linear combination of these filters to the ternary watermark. We consider the two most frequently occurring attacks: filtering and converting into mp3 format.

Keywords: digital watermarks, ternary coding, audio files, discrete cosine transform

Acknowledgments. This study was supported by the Kazan Federal University Strategic Academic Leadership Program.

References

1. Hua G., Huang J., Shi Y.Q., Goh J., Thing V.L.L. Twenty years of digital audio watermarking – a comprehensive review. Signal Process., 2016, vol. 128, pp. 222–242. doi: 10.1016/j.sigpro.2016.04.005.
2. Chauhan S., Rizvi S. A survey: Digital audio watermarking techniques and applications. Proc. 2013 4th Int. Conf. on Computer and Communication Technology (IC-CCT), 20–22 Sept., 2013. Allahabad, India, IEEE, 2013, pp. 185–192. doi: 10.1109/IC- CCT.2013.6749625.
3. Bajpai J., Kaur A. A literature survey – various audio watermarking techniques and their challenges. Proc. 2016 6th Int. Conf. – Cloud System and Big Data Engineering (Confluence), 14–15 Jan., 2016. Noida, India, IEEE, 2016, pp. 451–457. doi: 10.1109/CONFLU- ENCE.2016.7508162.
4. Xiang Y., Hua G., Yan B. Digital Audio Watermarking: Fundamentals, Techniques, and Challenges. Springer Singapore, 2017. xii, 90 p. doi: 10.1007/978-981-10-4289-8.
5. Thanki R. Advanced Techniques for Audio Watermarking. Springer Int. Publ., 2020. xv, 101 p. doi: 10.1007/978-3-030-24186-5.
6. Bassia P., Pitas I., Nikolaidis N. Robust audio watermarking in the time domain. IEEE Trans. Multimedia, 2001, vol. 3, no. 2, pp. 232–241. doi: 10.1109/6046.923822.
7. Xiang Y., Natgunanathan I., Peng D., Hua G., Liu B. Spread spectrum audio watermarking using multiple orthogonal PN sequences and variable embedding strengths and polarities. IEEE/ACM Trans. Audio, Speech, Lang. Process., 2018, vol. 26, no. 3, pp. 529– 539. doi: 10.1109/TASLP.2017.2782487.
8. Wang S., Yuan W., Wang J., Unoki M. Inaudible speech watermarking based on self-compensated echo-hiding and sparse subspace clustering. Proc. 2019 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP). Brighton, U. K., IEEE, 2019, pp. 2632–2636. doi: 10.1109/ICASSP.2019.8682352.
9. Swanson M.,D., Zhu B., Tewfik A.H., Boney L. Robust audio watermarking using perceptual masking. Signal Process., 2017, vol. 66, no. 3, pp. 337–355. doi: 10.1016/S0165- 1684(98)00014-0.
10. Pardhu T., Perly B. Digital image watermarking in frequency domain. Proc. 2016 Int. Conf. on Communication and Signal Processing (ICCSP), 6–8 Apr., 2016. Melmaruvathur, India, IEEE, 2016, pp. 208–211. doi: 10.1109/ICCSP.2016.7754123.
11. Erfani Y., Pichevar R., Rouat J. Audio watermarking using spikegram and a two-dictionary approach. IEEE Trans. Inf. Forensics Secur., 2017, vol. 12, no. 4, pp. 840–852. doi: 10.1109/TIFS.2016.2636094.
12. Subir, Joshi A.M. DWT-DCT based blind audio watermarking using Arnold scrambling and Cyclic codes. Proc. 2016 3rd Int. Conf. on Signal Processing and Integrated Networks (SPIN), 11–12 Feb., 2016. Noida, India, IEEE, 2016, pp. 79–84. doi: 10.1109/SPIN.2016.7566666.
13. Hwang M.J., Lee J., Lee M., Kang H.G. SVD-based adaptive QIM watermarking on stereo audio signals. IEEE Trans. Multimedia, 2018, vol. 20, no. 1, pp. 45–54. doi: 10.1109/TMM.2017.2721642.
14. Budiman G., Suksmono A., Danudirdjo D., Pawellang S. QIM-based audio watermarking with combined techniques of SWT-DST-QR-CPT using SS-based synchronization. 2018 Proc. 6th Int. Conf. on Information and Communication Technology (ICoICT), 3–5 May 2018. Bandung, Indones., IEEE, 2018, pp. 286–292. doi: 10.1109/ICoICT.2018.8528727.
15. Absalyamova K.S., Latypov R.Kh., Stolov E.L. Ternary code of melody and reliable audio watermarking. Proc. 2019 27th Telecommun. Forum (TELFOR), 26–27 Nov. 2019. Belgrad, Serbia, IEEE, 2019, pp. 1–4. doi: 10.1109/TELFOR48224.2019.8971187.
16. Latypov R.Kh., Stolov E.L. Ternary picture as watermark for audio files. Proc. 2020 3rd Int. Conf. on Computer Applications & Information Security (ICCAIS), 19–21 March, 2020. Er-Riyadh, Saudi Arabia, IEEE, 2020, pp. 1–6. doi: 10.1109/IC- CAIS48893.2020.9096786.
17. Unser A. Splines: A perfect fit for signal and image processing. IEEE Signal Process. Mag., 1999, vol. 16, no. 6, pp. 22–38. doi: 10.1109/79.799930.
18. Bender W., Gruhl D., Morimoto N., Lu A. Techniques for data hiding. IBM Syst. J., 1996, vol. 35, no. 3.4, pp. 313–336. doi: 10.1147/sj.353.0313.
19. Hua G., Goh J., Thing V.L.L. Cepstral analysis for the application of echo-based audio watermark detection. IEEE Trans. Inf. Forensics Secur., 2015, vol. 10, no. 9, pp. 1850– 1861. doi: 10.1109/TIFS.2015.2431997.
20. Latypov R., Stolov E. Speaker diarization based on speech signal approximation by step-function. Proc. 28th IEEE Conf. of Open Innovations Association FRUCT (FRUCT28), 2021, pp. 598–604. doi: 10.5281/zenodo.4514965.
21. Latypov R.Kh., Stolov E.L. Ternary coded melody as blind audio watermark. Telfor J., 2020, vol. 12, no. 1, pp. 28–33. doi: 10.5937/telfor2001028L.
22. Latypov R.Kh., Stolov E.L. Ternary echo hiding in audio files. Proc. 2020 28th Telecommun. Forum (TELFOR), 24–25 Nov., 2020. Belgrad, Serbia, 2020, pp. 1–4. doi: 10.1109/TELFOR51502.2020.9306575.
23. Virtanen P., Gommers R., Oliphant T.E., Haberland M., Reddy T., Cournapeau D., Burovski E., Peterson P., Weckesser W., Bright J., van der Walt S.J., Brett M., Wilson J., Millman K.J., Mayorov N., Nelson A.R.J., Jones E., Kern R., Larson E., Carey C.J., Polat I., Feng Y., Moore E.W., VanderPlas J., Laxalde D., Perktold J., Cimrman R., Henriksen I., Quintero E.A., Harris Ch.R., Archibald A.M., Ribeiro A.H., Pedregosa F., van Mulbregt P., SciPy 1.0 Contributors. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods, 2020, vol. 17, pp. 261–272. doi: 10.1038/s41592-019- 0686-2.
24. Robert J., Webbie M. et al. PyDub. 2018. Available at: http://pydub.com/.

Received January 19, 2021

Latypov Roustam Khafizovich, Doctor of Technical Sciences, Professor, Head of Department of System Analysis and Information Technologies

Kazan Federal University

ul. Kremlevskaya 18, Kazan, 420008 Russia E-mail: roustam.latypov@kpfu.ru

Stolov Evgeny L’vovich, Doctor of Technical Sciences, Professor, Leading Research Fellow of Research Laboratory of Computational Technologies and Computer Modeling

Kazan Federal University

ul. Kremlevskaya, 18, Kazan, 420008 Russia E-mail: ystolov@list.ru

Контент доступен под лицензией Creative Commons Attribution 4.0 License.