Abstract
In this paper, a steganography–steganalysis model for quantum audio signals is presented. This model consists of two separate parts of steganography and steganalysis. In the steganography part, to increase the imperceptibility and enhance the undetectability of the least significant bit method, first, the quantum host audio signal is divided into quantum frames of the specified length, and then, using the triangular number sequence, some samples of each frame are selected for the embedding operation. Finally, the embedding process is carried out within the least significant fractional qubit of the amplitude information of the selected samples. The steganalysis part also includes a quantum universal steganalyzer for detecting the embedding operations of the steganography part and a feature extraction module for calculating the power feature of quantum audio signal frames. The recognition operation is based on the quantum K-nearest neighbor algorithm and the Hamming distance criterion. All quantum circuits of the steganography and steganalysis parts of the proposed model were simulated and then tested and evaluated using different audio files. Detection accuracy of over 90% indicates the accuracy and efficiency of the proposed method in quantum audio steganography detection in the context of quantum communication networks.
Similar content being viewed by others
References
E. Aïmeur, G. Brassard, S. Gambs, Quantum speed-up for unsupervised learning. Mach. Learn. 90(2), 261–287 (2013)
A.H. Ali, L.E. George, A. Zaidan, M.R. Mokhtar, High capacity, transparent and secure audio steganography model based on fractal coding and chaotic map in temporal domain. Multimed. Tools Appl. 77(23), 31487–31516 (2018)
G.E. Andrews, Number Theory (Courier Corporation, North Chelmsford, 1994)
K. Bailey, K. Curran, J. Condell, Evaluation of pixel-based steganography and stegodetection methods. Imaging Sci. J. 52(3), 131–150 (2004)
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, S. Lloyd, Quantum machine learning. Nature 549(7671), 195 (2017)
R. Böhme, Advanced Statistical Steganalysis (Springer, Cham, 2010)
R. Chandramouli, M. Kharrazi, N. Memon, Image steganography and steganalysis: concepts and practice. in International Workshop on Digital Watermarking (Springer, 2003), pp. 35–49
K. Chen, F. Yan, A.M. Iliyasu, J. Zhao, Exploring the implementation of steganography protocols on quantum audio signals. Int. J. Theor. Phys. 57(2), 476–494 (2018)
K. Chen, F. Yan, A.M. Iliyasu, J. Zhao, Dual quantum audio watermarking schemes based on quantum discrete cosine transform. Int. J. Theor. Phys. 58(2), 502–521 (2019)
D. Deutsch, Uncertainty in quantum measurements. Phys. Rev. Lett. 50(9), 631 (1983)
V. Dunjko, H.J. Briegel, Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep. Prog. Phys. 81(7), 074001 (2018)
V. Dunjko, J.M. Taylor, H.J. Briegel, Quantum-enhanced machine learning. Phys. Rev. Lett. 117(13), 130501 (2016)
S.E. El-Khamy, N.O. Korany, M.H. El-Sherif, A security enhanced robust audio steganography algorithm for image hiding using sample comparison in discrete wavelet transform domain and RSA encryption. Multimed. Tools Appl. 76(22), 24091–24106 (2017)
A.A.A. EL-Latif, B. Abd-El-Atty, S.E. Venegas-Andraca, A novel image steganography technique based on quantum substitution boxes. Opt. Laser Technol. 116, 92–102 (2019)
S. Heidari, M.R. Pourarian, R. Gheibi, M. Naseri, M. Houshmand, Quantum red–green–blue image steganography. Int. J. Quantum Inf. 15(05), 1750039 (2017)
N. Jiang, L. Wang, A quantum image information hiding algorithm based on Moiré pattern. Int. J. Theor. Phys. 54, 1021–1032 (2014)
N. Jiang, N. Zhao, L. Wang, LSB based quantum image steganography algorithm. Int. J. Theor. Phys. 55(1), 107–123 (2016)
J.I. Latorre, Image compression and entanglement (2005). arXiv:quant-ph/0510031
P.Q. Le, F. Dong, K. Hirota, A flexible representation of quantum images for polynomial preparation, image compression, and processing operations. Quantum Inf. Process. 10(1), 63–84 (2011)
P. Li, B. Wang, H. Xiao, X. Liu, Quantum representation and basic operations of digital signals. Int. J. Theor. Phys. 57(10), 3242–3270 (2018)
S. Lloyd, M. Mohseni, P. Rebentrost, Quantum algorithms for supervised and unsupervised machine learning (2013). arXiv:1307.0411
R.G. Lyons, Understanding Digital Signal Processing, 3rd edn. (Pearson Education India, Delhi, 2011)
M. Naseri, S. Heidari, M. Baghfalaki, R. Gheibi, J. Batle, A. Farouk, A. Habibi, A new secure quantum watermarking scheme. Optik 139, 77–86 (2017)
M.Y. Nejad, M. Mosleh, S.R. Heikalabad, An LSB-based quantum audio watermarking using MSB as arbiter. Int. J. Theor. Phys. 58, 3828–3851 (2019)
A.V. Oppenheim, Discrete-Time Signal Processing (Pearson Education India, Delhi, 1999)
Z.-G. Qu, H.-X. He, T. Li, Novel quantum watermarking algorithm based on improved least significant qubit modification for quantum audio. Chin. Phys. B 27(1), 010306 (2018)
Z. Qu, Z. Cheng, W. Liu, X. Wang, A novel quantum image steganography algorithm based on exploiting modification direction. Multimed. Tools Appl. 78, 7981–8001 (2018)
Z. Qu, Z. Cheng, W. Liu, X. Wang, A novel quantum image steganography algorithm based on exploiting modification direction. Multimed. Tools Appl. 78(7), 7981–8001 (2019)
P. Rebentrost, M. Mohseni, S. Lloyd, Quantum support vector machine for big data classification. Phys. Rev. Lett. 113(13), 130503 (2014)
Y. Ruan, H. Chen, J. Tan, X. Li, Quantum computation for large-scale image classification. Quantum Inf. Process. 15(10), 4049–4069 (2016)
Y. Ruan, X. Xue, H. Liu, J. Tan, X. Li, Quantum algorithm for k-nearest neighbors classification based on the metric of hamming distance. Int. J. Theor. Phys. 56(11), 3496–3507 (2017)
E. Şahin, İ. Yilmaz, A novel quantum steganography algorithm based on LSBq for multi-wavelength quantum images. Quantum Inf. Process. 17(11), 319 (2018)
E. Şahin, İ. Yilmaz, QRMA: quantum representation of multichannel audio. Quantum Inf. Process. 18(7), 209 (2019)
J. Sang, S. Wang, Q. Li, A novel quantum representation of color digital images. Quantum Inf. Process. 16(2), 42 (2017)
M. Schuld, I. Sinayskiy, F. Petruccione, Quantum computing for pattern classification. in Pacific Rim International Conference on Artificial Intelligence 2014 (Springer, 2014), pp. 208–220
G.J. Simmons, The prisoners’ problem and the subliminal channel, in Advances in Cryptology, ed. by D. Chaum (Springer, Boston, 1984), pp. 51–67
B. Sun, A.M. Iliyasu, F. Yan, F. Dong, K. Hirota, An RGB multi-channel representation for images on quantum computers. J. Adv. Comput. Intell. Intell. Inf. 17(3), 404–417 (2013)
A.Z. Tirkel, G. Rankin, R. Van Schyndel, W. Ho, N. Mee, C.F. Osborne, Electronic watermark. in Digital Image Computing, Technology Applications (1993), pp. 666–673
C.A. Trugenberger, Probabilistic quantum memories. Phys. Rev. Lett. 87(6), 067901 (2001)
V. Vedral, A. Barenco, A. Ekert, Quantum networks for elementary arithmetic operations. Phys. Rev. A 54(1), 147 (1996)
S.E. Venegas-Andraca, S. Bose, Storing, processing, and retrieving an image using quantum mechanics. in Quantum Information and Computation 2003 (International Society for Optics and Photonics, 2003), pp. 137–148
S.E. Venegas-Andraca, J. Ball, Processing images in entangled quantum systems. Quantum Inf. Process. 9(1), 1–11 (2010)
J. Wang, QRDA: quantum representation of digital audio. Int. J. Theor. Phys. 55(3), 1622–1641 (2016)
D. Wang, Z.-H. Liu, W.-N. Zhu, S.-Z. Li, Design of quantum comparator based on extended general Toffoli gates with multiple targets. Comput. Sci. 39(9), 302–306 (2012)
S. Wang, J. Sang, X. Song, X. Niu, Least significant qubit (LSQb) information hiding algorithm for quantum image. Measurement 73, 352–359 (2015)
J. Wiśniewska, M. Sawerwain, Recognizing the pattern of binary Hermitian matrices by quantum kNN and SVM methods. Vietnam J. Comput. Sci. 5, 197–204 (2018)
W.K. Wootters, W.H. Zurek, A single quantum cannot be cloned. Nature 299(5886), 802 (1982)
F. Yan, A.M. Iliyasu, Y. Guo, H. Yang, Flexible representation and manipulation of audio signals on quantum computers. Theor. Comput. Sci. 752, 71–85 (2017)
Y. Zhang, K. Lu, Y. Gao, M. Wang, NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(8), 2833–2860 (2013)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chaharlang, J., Mosleh, M. & Rasouli Heikalabad, S. A Novel Quantum Audio Steganography–Steganalysis Approach Using LSFQ-Based Embedding and QKNN-Based Classifier. Circuits Syst Signal Process 39, 3925–3957 (2020). https://doi.org/10.1007/s00034-020-01345-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-020-01345-6