A New High-Capacity Audio Watermarking Based on Wavelet Transform using the Golden Ratio and TLBO Algorithm
Subject Areas : Signal Processing
Ali Zeidi joudaki
1
,
Marjan Abdeyazdan
2
*
,
Mohammad Mosleh
3
,
Mohammad Kheyrandish
4
1 - Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
2 - Department of Computer Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
3 - Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
4 - Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
Keywords: Audio Watermarking, Discrete Wavelet Transform (DWT), High capacity, TLBO Optimization Algorithm,
Abstract :
Digital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for researchers and scholars. In this paper, we presented a new high-capacity, clear, and robust audio signaling scheme based on the DWT conversion synergy and golden ratio advantages using the TLBO algorithm. We used the TLBO algorithm to determine the effective frame length and embedded range, and the golden ratio to determine the appropriate embedded locations for each frame. First, the main audio signal was broken down into several sub-bands using a DWT in a specific frequency range. Since the human auditory system is not sensitive to changes in high-frequency bands, to increase the clarity and capacity of these sub-bands to embed bits we used the watermark signal. Moreover, to increase the resistance to common attacks, we framed the high-frequency bandwidth and then used the average of the frames as a key value. Our main idea was to embed an 8-bit signal simultaneously in the host signal. Experimental results showed that the proposed method is free from significant noticeable distortion (SNR about 29.68dB) and increases the resistance to common signal processing attacks such as high pass filter, echo, resampling, MPEG (MP3), etc.
[1] Swanson MD, Zhu B, Tewfik AH, Boney L. Robust audio watermarking using perceptual masking. Signal processing. 1998, pp.337-355.
[2] Liu W, Hu AQ. A sub-band excitation substitute based scheme for narrowband speech watermarking. Frontiers of Information Technology & Electronic Engineering. 2017, pp.627-643.
[3] Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K. A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE transactions on information forensics and security. 2016, pp.2594-2608.
[4] Nguyen TS, Chang CC, Yang XQ. A reversible image authentication scheme based on fragile watermarking in the discrete wavelet transform domain. AEU-International Journal of Electronics and Communications. 2016, pp.1055-1061.
[5] Sun L, Xu J, Liu S, Zhang S, Li Y, Shen CA. A robust image watermarking scheme using Arnold transform and BP neural network. Neural Computing and Applications. 2018, pp.2425-2440.
[6] slam M, Roy A, Laskar RH. SVM-based robust image watermarking technique in LWT domain using different sub-bands. Neural Computing and Applications. 2020, pp.1379-1403.
[7] Katzenbeisser S, Petitcolas FA. Digital watermarking. Artech House, London. 2000; 2.
[8] Nejad MY, Mosleh M, Heikalabad SR. LSB-based quantum audio watermarking using MSB as arbiter. International Journal of Theoretical Physics. 2019, pp.3828-3851.
[9] Rasti P, Samiei S, Agoyi M, Escalera S, Anbarjafari G. Robust non-blind color video watermarking using QR decomposition and entropy analysis. Journal of Visual Communication and Image Representation. 2016, pp.838-847.
[10] Bhardwaj A, Verma VS, Jha RK. Robust video watermarking using significant frame selection based on the coefficient difference of lifting wavelet transform. Multimedia Tools and Applications. 2018, pp.19659-19678.
[11] Ali M, Ahn CW. An optimal image watermarking approach through cuckoo search algorithm in the wavelet domain. International Journal of System Assurance Engineering and Management. 2018, pp.602-611.
[12] Mehta R, Rajpal N, Vishwakarma VP. Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization. International Journal of Machine Learning and Cybernetics. 2018, pp.145-161.
[13] Fallahpour M, Megias D. Robust high-capacity audio watermarking based on FFT amplitude modification. IEICE TRANSACTIONS on Information and Systems. 2010, pp.87-93.
[14] Kumsawat P. An efficient digital audio watermarking scheme based on genetic algorithm. In2010 10th International Symposium on Communications and Information Technologies 2010, pp. 481-485.
[15] Martínez-Noriega R, Nakano M, Kurkoski B, Yamaguchi K. High payload audio watermarking: toward channel characterization of MP3 compression. Journal of Information Hiding and Multimedia Signal Processing. 2011, pp. 91-107.
[16] Bath V, Sengupta I, Das A. A new audio watermarking scheme based on singular value decomposition and quantization. Circuits, Systems, and Signal Processing. 2011, pp.915-927.
[17] Fallahpour M, Megías D. High capacity logarithmic audio watermarking based on the human auditory system. In2012 IEEE International Symposium on Multimedia 2012, pp. 28-31.
[18] Mohsenfar SM, Mosleh M, Barati A. Audio watermarking method using QR decomposition and genetic algorithm. Multimedia Tools and Applications. 2015, pp.759-779.
[19] Hu HT, Hsu LY, Chou HH. Perceptual-based DWPT-DCT framework for selective blind audio watermarking. Signal processing. 2014, pp.316-327.
[20] Chen ST, Hsu CY, Huang HN. Wavelet-domain audio watermarking using optimal modification on low-frequency amplitude. IET signal processing. 2015, pp.166-176.
[21] Nair UR, Birajdar GK. Audio watermarking in wavelet domain using Fibonacci numbers. In2016 International Conference on Signal and Information Processing (IConSIP), 2016, pp. 1-5.
[22] Erfani Y, Pichevar R, Rouat J. Audio watermarking using spikegram and a two-dictionary approach. IEEE transactions on information forensics and security. 2016, pp.840-852.
[23] Hemis M, Boudraa B, Megías D, Merazi-Meksen T. Adjustable audio watermarking algorithm based on DWPT and psychoacoustic modeling. Multimedia Tools and Applications. 2018, pp.11693-11725.
[24] Kaur A, Dutta MK. An optimized high payload audio watermarking algorithm based on LU-factorization. Multimedia Systems. 2018, pp.341-353.
[25] Saadi S, Merrad A, Benziane A. Novel secured scheme for blind audio/speech norm-space watermarking by Arnold algorithm. Signal Processing. 2019, pp.74-86.
[26] Pourhashemi SM, Mosleh M, Erfani Y. Audio watermarking based on the synergy between Lucas regular sequence and Fast Fourier Transform. Multimedia Tools and Applications. 2019, pp.22883-22908.
[27] Mahdi Mosleh, Saeed Setayeshi , Behrang Barekatain, Mohammad Mosleh. A novel audio watermarking scheme based on fuzzy inference system in DCT domain. Multimedia Tools and Applications. 2021, pp. 20423–20447 .
[28] Ahmed Hussain Ali, Loay Edwar George, Mohd Rosmadi Mokhtar. An Adaptive High Capacity Model for Secure Audio Communication Based on Fractal Coding and Uniform Coefficient Modulation. Circuits, Systems, and Signal Processing. 2020. pp. pages5198–5225.
[29] Cai, Y.M., Guo, W.Q. and Ding, H.Y., 2013. Dwt-svd. Journal of Software, 8(7), p.1801.
[30] Brannock, E., Weeks, M. and Harrison, R., 2008, April. Watermarking with wavelets: simplicity leads to robustness. In IEEE SoutheastCon 2008, pp. 587-592.
[31] Pandey, V., 2014. Analysis of image compression using wavelets. International Journal of Computer Applications, 103(17).
[32] Lerch, A. (2002). Zplane development, EAQUAL-Evaluate Audio QUALity, version: 0.1. 3alpha. Retrieved July 2018, from http://www.mp3-tech.org/programmer/misc.html.
[33] Akhlaghi M, Emami F, Nozhat N. Binary TLBO algorithm assisted for designing plasmonic nano bi-pyramids-based absorption coefficient. Journal of Modern Optics. 2014, pp.1092-1096.
[34] Akhlaghi M, Emami F, Nozhat N, "Binary TLBO algorithm assisted for designing plasmonic nano bi-pyramids-based absorption coefficient", Journal of Modern Optics, 2014, pp.1092-1096.
[35] Akhlaghi M. Optimization of the plasmonic nano-rods-based absorption coefficient using TLBO algorithm. Optik. 2015, pp.5033-5037.
[36] Balvasi M, Akhlaghi M, Shahmirzaee H, "Binary TLBO algorithm assisted to investigate the supper scattering plasmonic nanotubes", Superlattices and Microstructures. 2016, pp.26-33.
[37] Kaboli M, Akhlaghi M, “Binary teaching-learning-based optimization algorithm was used to investigate the superscattering plasmonic nanodisk", Optics and Spectroscopy. 2016, pp.958-63.