Robust image-in-audio watermarking technique based on DCT-SVD transform
Tóm tắt
In this paper, a robust and highly imperceptible audio watermarking technique is presented based on discrete cosine transform (DCT) and singular value decomposition (SVD). The low-frequency components of the audio signal have been selectively embedded with watermark image data making the watermarked audio highly imperceptible and robust. The imperceptibility of proposed methods is evaluated by computing signal-to-noise ratio and by conducting subjective listening tests. The robustness of proposed technique is evaluated by computing bit error rate and average information loss in retrieved watermark image subjected to MP3 compression, AWGN, re-sampling, re-quantization, amplitude scaling, low-pass filtering, and high-pass filtering attacks with high data payload of 6 kbps. The information-theoretic approach is used to model the proposed watermarking technique as discrete memoryless channel. The Shannon’s entropy concept is used to highlight the robustness of proposed technique by computing the information loss in retrieved watermarked image.
Tài liệu tham khảo
M. Arnold, M. Schmucker, S. D. Wolthusen, Techniques and Applications of Digital Watermarking and Content Protection (Artech House, Inc., Norwood, 2003).
H. J. Kim, Y. H. Choi, J. Seok, J. Hong, Audio watermarking techniques. Intell. Watermarking Tech.7:, 185 (2004).
M. Fallahpour, D. Megias, Audio watermarking based on fibonacci numbers. IEEE/ACM Trans. Audio, Speech, Lang. Process.23(8), 1273–1282 (2015). https://doi.org/10.1109/TASLP.2015.2430818.
W. Bender, D. Gruhl, N. Morimoto, A. Lu, Techniques for data hiding. IBM Syst. J.35(3.4), 313–336 (1996).
N. Cvejic, T. Seppanen, in 2002 IEEE Workshop on Multimedia Signal Processing. Increasing the capacity of LSB-based audio steganography (St. Thomas, 2002), pp. 336–338. https://doi.org/10.1109/MMSP.2002.1203314, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1203314&isnumber=27103.
P. Bassia, I. Pitas, N. V, Robust audio watermarking in the time domain. IEEE Trans. Multimed.3(2), 232–241 (2001). https://doi.org/10.1109/6046.923822.
W. -N. Lie, L. -C. Chang, Robust and high-quality time-domain audio watermarking based on low-frequency amplitude modification. IEEE Trans. Multimed.8(1), 46–59 (2006).
D. Cai, K. Gopalan, in IEEE International Conference on Electro/Information Technology. Audio watermarking using bit modification of voiced or unvoiced segments (Milwaukee, 2014), pp. 491–494. https://doi.org/10.1109/EIT.2014.6871813, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6871813&isnumber=6871745.
Y. Erfani, S. Siahpoush, Robust audio watermarking using improved TS echo hiding. Digit. Signal Process.19(5), 809–814 (2009).
G. Hua, J. Goh, V. L. L. Thing, Cepstral analysis for the application of echo-based audio watermark detection. IEEE Trans. Inf. Forensics Secur.10(9), 1850–1861 (2015). https://doi.org/10.1109/TIFS.2015.2431997.
A. Kanhe, G. Aghila, C. Y. S. Kiran, C. H. Ramesh, G. Jadav, M. G. Raj, in 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Robust Audio steganography based on Advanced Encryption standards in temporal domain (Kochi, 2015), pp. 1449–1453. https://doi.org/10.1109/ICACCI.2015.7275816, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7275816&isnumber=7275573.
G. Hua, J. Huang, Y. Q. Shi, J. Goh, V. L. L. Thing, Twenty years of digital audio watermarking—a comprehensive review. Signal Process.128:, 222–242 (2016). https://doi.org/10.1016/j.sigpro.2016.04.005.
F. Djebbar, B. Ayad, K. A. Meraim, H. Hamam, Comparative study of digital audio steganography techniques. EURASIP J. Audio, Speech, Music Process.2012(1), 25 (2012). https://doi.org/10.1186/1687-4722-2012-25.
M. Fallahpour, D. Megias, High capacity audio watermarking using FFT amplitude interpolation. IEICE Electron. Express. 6(14), 1057–1063 (2009).
R. K. Jha, B. Soni, K. Aizawa, Logo extraction from audio signals by utilization of internal noise. IETE J. Res.59(3), 270–279 (2013). https://doi.org/10.4103/03772063.2013.10876505.
M. Fallahpour, D. Megias, Robust high-capacity audio watermarking based on FFT amplitude modification. IEICE Trans. Inf. Syst.93(1), 87–93 (2010).
S. V. Dhavale, R. S. Deodhar, D. Pradhan, L. M. Patnaik, State transition based embedding in cepstrum domain for audio copyright protection. IETE J. Res.61(1), 41–55 (2015). https://doi.org/10.1080/03772063.2014.987704.
X. Y. Wang, H. Zhao, A novel synchronization invariant audio watermarking scheme based on dwt and dct. IEEE Trans. Signal Process.54(12), 4835–4840 (2006). https://doi.org/10.1109/TSP.2006.881258.
J. Li, T. Wu, in 2015 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS). Robust audio watermarking scheme via QIM of correlation coefficients using LWT and QR decomposition (Chengdu, 2015), pp. 1–6. https://doi.org/10.1109/ICCSS.2015.7281138, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7281138&isnumber=7281133.
A. Kanhe, G. Aghila, in Proceedings of the International Conference on Informatics and Analytics, ICIA-16. DCT Based Audio Steganography in Voiced and Un-voiced Frames (ACMNew York, 2016), pp. 47:1–47:4. https://doi.org/10.1145/2980258.2980360.
Z. Chen, C. Zhao, G. Geng, F. Yin, An audio watermark-based speech bandwidth extension method. EURASIP J. Audio, Speech, Music Process.2013(1), 10 (2013). https://doi.org/10.1186/1687-4722-2013-10.
H. Ozer, Sankur, N. Memon, in Proceedings of the 7th Workshop on Multimedia and Security. MM&Sec ’05. An SVD-based audio watermarking technique (ACMNew York, 2005), pp. 51–56. https://doi.org/10.1145/1073170.1073180.
A. -H. Ali, An imperceptible and robust audio watermarking algorithm. EURASIP J. Audio, Speech, Music Process.2014(1), 37 (2014). https://doi.org/10.1186/s13636-014-0037-2.
V. Bhat, I. Sengupta, A. Das, A new audio watermarking scheme based on singular value decomposition and quantization. Circ. Syst. Signal Process.30(5), 915–927 (2011).
H. -T. Hu, L. -Y. Hsu, A DWT-based rational dither modulation scheme for effective blind audio watermarking. Circ. Syst. Signal Process.35(2), 553–572 (2016). https://doi.org/10.1007/s00034-015-0074-9.
M. J. Hwang, J. Lee, M. Lee, H. G. Kang, SVD-based adaptive QIM watermarking on stereo audio signals. IEEE Trans. Multimed.20(1), 45–54 (2018). https://doi.org/10.1109/TMM.2017.2721642.
H. -T. Hu, L. -Y. Hsu, Incorporating spectral shaping filtering into DWT-based vector modulation to improve blind audio watermarking. Wirel. Pers. Commun.94(2), 221–240 (2017). https://doi.org/10.1007/s11277-016-3178-z.
A. Kaur, M. K. Dutta, An optimized high payload audio watermarking algorithm based on LU-factorization. Multimedia Systems. 24(3), 341–353 (2018). https://doi.org/10.1007/s00530-017-0545-x.
H. -T. Hu, J. -R. Chang, Efficient and robust frame-synchronized blind audio watermarking by featuring multilevel DWT and DCT. Clust Comput.20(1), 805–816 (2017). https://doi.org/10.1007/s10586-017-0770-2.
H. -T. Hu, S. -J. Lin, L. -Y. Hsu, Effective blind speech watermarking via adaptive mean modulation and package synchronization in DWT domain. EURASIP J. Audio, Speech, Music Process.2017(1), 10 (2017). https://doi.org/10.1186/s13636-017-0106-4.
A. R. Elshazly, M. E. Nasr, M. M. Fouad, F. S Abdel-Samie, High payload multi-channel dual audio watermarking algorithm based on discrete wavelet transform and singular value decomposition. Int. J. Speech Technol.20(4), 951–958 (2017). https://doi.org/10.1007/s10772-017-9462-9.
Q. Wu, M. Wu, A novel robust audio watermarking algorithm by modifying the average amplitude in transform domain.Appl. Sci. (2076-3417). 8(5) (2018).
A. Sverdlov, S. Dexter, A. M. Eskicioglu, in Signal Processing Conference, 2005 13th European. Robust DCT-SVD domain image watermarking for copyright protection: embedding data in all frequencies (IEEE, 2005), pp. 1–4.
L. Rabiner, B. -H. Juang, Fundamentals of Speech Recognition (Prentice-Hall, Inc., Upper Saddle River NJ USA, 1993).
R. Bachu, S. Kopparthi, B. Adapa, B. D. Barkana, in Electrical Engineering Department School of Engineering. Separation of voiced and unvoiced using zero crossing rate and energy of the speech signal (University of Bridgeport, 2005), pp. 1–7.
Y. Hu, P. C. Loizou, Subjective comparison and evaluation of speech enhancement algorithms. Speech Comm.49(7), 588–601 (2007).
Y. Hu, P. C. Loizou, Evaluation of objective quality measures for speech enhancement. IEEE Trans. Speech Audio Process.16(1), 229–238 (2008).
J. Ma, Y. Hu, P. C. Loizou, Objective measures for predicting speech intelligibility in noisy conditions based on new band- importance functions. J. Acoust. Soc. Am.125(5), 3387–3405 (2009).
Huang P.S., S. D. Chen, P. Smaragdis, M. Hasegawa-Johnson, in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singing-voice separation from monaural recordings using robust principal component analysis, (2012), pp. 57–60.
S. Rekik, D. Guerchi, S. -A. Selouani, H. Hamam, Speech steganography using wavelet and fourier transforms. EURASIP J. Audio, Speech, Music Process.2012:, 20 (2012). https://doi.org/10.1186/1687-4722-2012-20.
Y. Ephraim, D. Malah, Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Trans. Acoust. Speech, Signal Process.33(2), 443–445 (1985). https://doi.org/10.1109/TASSP.1985.1164550.
P. Moulin, J. A. O’Sullivan, Information-theoretic analysis of information hiding. IEEE Trans. Inf. Theory. 49(3), 563–593 (2003). https://doi.org/10.1109/TIT.2002.808134.
D. -Y. Tsai, Y. Lee, E. Matsuyama, Information entropy measure for evaluation of image quality. J. Digit. Imaging. 21(3), 338–347 (2008).
W. K. McDowell, W. B. Mikhael, A. P. Berg, in 2012 Proceedings of IEEE Southeastcon. Efficiency of the KLT on voiced amp: unvoiced speech as a function of segment size, (2012), pp. 1–5. https://doi.org/10.1109/SECon.2012.6197063.