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2020, vol. 12, br. 1, str. 28-33
Ternary coded melody as blind audio watermark
(naslov ne postoji na srpskom)
Kazan Federal University, Kazan, Russia

e-adresaRoustam.Latypov@kpfu.ru, ystolov@list.ru
Projekat:
The work is performed according to the Russian Government Program of Competitive Growth of Kazan Federal University. The Russian Science Foundation grant No19-18-00202 also supports this work

Ključne reči: audio watermarks; B-spline; blind embedding; neural net; ternary coded melody
Sažetak
(ne postoji na srpskom)
In this paper, we developed a new technique for blind embedding of ternary coded watermarks into audio files. Usage of ternary coding increases payload of the method that can be considered as an advantage against binary-coded watermarks. A well-known melody is presented as a sequence of ternary digits (trits) and is used as a watermark. This sequence is embedded into the time domain of a host audio file through amplitude modulation and B-splines. There is a version of that procedure where the clean copy of the container is necessary for extraction watermark [1]. In our approach, we exclude that container and convert the method into a blind one. The strong correlation between neighbor samples in the container is used to this end. A procedure based on neuron net is suggested for enhancement perception of ternary coded music. In this case, we exploit the correlation between samples in the watermark melody. It is supposed that a person checks the mark's existence, and he/she can recognize the melody even after significant distortions. The resistance of the technique to the most successful attacks is investigated. The paper is an extended version of the conference paper [1].
Reference
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*** TensorFlow package manual. www.tensorflow.org
*** PyDub package manual. http://pydub.com
*** NumPy package manual. https://numpy.org
*** SciPy package manual. https:scipy.org
Absalyamova, K.S., Latypov, R.K., Stolov, E.L. (2019) Ternary code of melody and reliable audio watermarking. u: 27th Telecommunications Forum (TELFOR), November, Belgrade: IEEE
Chollet, F. (2017) Deep learning with Python. Manning Publications
Hu, H., Lee, T. (2019) High-performance self-synchronous blind audio watermarking in a unified FFT framework. IEEE Access, vol. 7, pp. 19063-19076, Feb
Hua, G., Huang, J., Shi, Y.Q., Goh, J., Thing, V.L.L. (2016) Twenty years of digital audio watermarking: A comprehensive review. Signal Processing, 128, 222-242
Kabal, P. (2002) An examination and interpretation of ITU-RBS.1387: Perceptual evaluation of audio quality. http://wwwmmsp.ece.mcgill.ca/Documents/Reports/2002/KabalR2002v2.pdf
Kaur, A., Dutta, M.K. (2018) A blind watermarking algorithm for audio signals in multi-resolution and singular value decomposition. u: Computational Intelligence and Communication Technology, (CICT 2018), Int. Conf, Proc, IEEE, 1-5
Kirovski, D., Malvar, H. (2001) Robust spread-spectrum audio watermarking. u: Acoustics, Speech, and Signal Proc. (ICASSP'01), Proc, IEEE, vol. 3, pp. 1345-1348, Feb
Sakai, H., Iwaki, M. (2018) Audio watermarking method based on phase-shifting having robustness against band-pass filtering attacks. u: 7th Global Conf. on Consumer Electronics (GCCE 2018), Proc, IEEE, 343-346
Unser, A. (1999) Splines: A perfect fit for signal and image processing. IEEE Signal Processing Magazine, 16(6), 22-38, Nov
 

O članku

jezik rada: engleski
vrsta rada: neklasifikovan
DOI: 10.5937/telfor2001028L
primljen: 07.05.2020.
revidiran: 03.06.2020.
prihvaćen: 04.07.2020.
objavljen: 31.07.2020.
objavljen u SCIndeksu: 09.10.2020.

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