A METHOD OF RECURSIVE LOW-FREQUENCY INTEGRATION OF TEXT INFORMATION INTO A STEGANOGRAPHIC AUDIO CONTAINER BASED ON DAUBECHIES WAVELET FILTERS
DOI:
https://doi.org/10.18372/2310-5461.57.17443Keywords:
steganography, text information, wavelet-transform, audiosignal, wavelet coefficientsAbstract
The developed method of recursive low-frequency integration of text information into a steganographic audio container based on Daubechies wavelet filters acquires a deep meaning in the conditions of an attacker’s use of deliberate unauthorized manipulations with a steganocoded audio signal with the aim of distorting the text information embedded in it, i.e. making its semantic structures unintelligible. The main of such manipulations is the application of various audio signal compression algorithms, but not with the aim of removing its uninformative components, which, according to the human psychophysiological model of sound perception, are beyond the threshold of hearing, and with the aim of removing the text information hidden in the audio signal by deliberately introducing distortions. The difference between the developed method and the existing ones is that in the existing methods of steganographic information hiding based on the wavelet transform, text information is usually integrated into high-frequency wavelet coefficients without scalar product with wavelet filters, and in the developed method, it is proposed to use recursive embedding in low-frequency wavelet coefficients with subsequent scalar product with orthogonal wavelet Daubechies filters of low and high frequencies, which allows to increase the absolute spectral power of the hidden text information. Further studies showed that the developed method significantly increases the resistance of the steganosystem to intentional or passive interventions for the purpose of recoding with a lower information transfer rate, but at the same time, one hundred percent integrity of the text information is ensured at the stage of its extraction from the audio signal, with sufficiently high sound quality indicators.
References
P. N. Basu and T. Bhowmik, “On Embedding of Text in Audio A Case of Steganography,” 2010 International Conference on Recent Trends in Information, Telecommunication and Computing, 2010, pp. 203-206, doi: 10.1109/ITC.2010.16.
S. B. Sadkhan, A. A. Mahdi and R. S. Mohammed, “Recent Audio Steganography Trails and its Quality Measures,” 2019 First International Conference of Computer and Applied Sciences (CAS), 2019, pp. 238-243, doi: 10.1109/CAS47993.2019.9075778.
S. Ahani, S. Ghaemmaghami and Z. J. Wang, “A Sparse Representation-Based Wavelet Domain Speech Steganography Method,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 1, pp. 80-91, Jan. 2015, doi: 10.1109/TASLP.2014.2372313.
Q. Liu, A. H. Sung and M. Qiao, “Temporal Derivative-Based Spectrum and Mel-Cepstrum Audio Steganalysis,” in IEEE Transactions on Information Forensics and Security, vol. 4, no. 3, pp. 359-368, Sept. 2009, doi: 10.1109/TIFS.2009.2024718.
M. Anwar, M. Sarosa and E. Rohadi, “Audio Steganography Using Lifting Wavelet Transform and Dynamic Key,” 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 2019, pp. 133-137, doi: 10.1109/ICAIIT.2019.8834579.
T. Narasimmalou and J. R. Allen, “Optimized discrete wavelet transform based steganography,” 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2012, pp. 88-91, doi: 10.1109/ICACCCT.2012.6320747.
P. M. Kumar and K. Srinivas, “Real Time Implementation of Speech Steganography,” 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), 2019, pp. 365-369, doi: 10.1109/ICSSIT46314.2019.8987785.
V. Varuikhin and A. Levina, “Steganographic Information Hiding Method Based on Double Wavelet Transform,” 2022 11th Mediterranean Conference on Embedded Computing (MECO), 2022, pp. 1-5, doi: 10.1109/MECO55406.2022.9797168.
E. Emad, A. Safey, A. Refaat, Z. Osama, E. Sayed and E. Mohamed, “A secure image steganography algorithm based on least significant bit and integer wavelet transform,” in Journal of Systems Engineering and Electronics, vol. 29, no. 3, pp. 639-649, June 2018, doi: 10.21629/JSEE.2018.03.21.
W. Jang and W. Lee, “Detecting Wireless Steganography With Wavelet Analysis,” in IEEE Wireless Communications Letters, vol. 10, no. 2, pp. 383-386, Feb. 2021, doi: 10.1109/LWC.2020.3032032.
K. Zhiweil, L. Jing and H. Yigang, “Steganography based on wavelet transform and modulus function,” in Journal of Systems Engineering and Electronics, vol. 18, no. 3, pp. 628-632, Sept. 2007, doi: 10.1016/S1004-4132(07)60139-X.
R. J. Mstafa, K. M. Elleithy and E. Abdelfattah, “A Robust and Secure Video Steganography Method in DWT-DCT Domains Based on Multiple Object Tracking and ECC,” in IEEE Access, vol. 5, pp. 5354-5365, 2017, doi: 10.1109/ACCESS.2017.2691581.
O. Lavrynenko, G. Konakhovych and D. Bakhtiiarov, “Method of voice control functions of the UAV,” 2016 4th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), 2016, pp. 47-50, doi: 10.1109/MSNMC.2016.7783103.
D. Bakhtiiarov, G. Konakhovych and O. Lavrynenko, “Protected system of radio control of unmanned aerial vehicle,” 2016 4th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), 2016, pp. 196-199, doi: 10.1109/MSNMC.2016.7783141.
D. I. Bakhtiiarov, G. F. Konakhovych and O. Y. Lavrynenko, “An Approach to Modernization of the Hat and COST 231 Model for Improvement of Electromagnetic Compatibility in Premises for Navigation and Motion Control Equipment,” 2018 IEEE 5th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), 2018, pp. 271-274, doi: 10.1109/MSNMC.2018.8576260.
O. Lavrynenko, A. Taranenko, I. Machalin, Y. Gabrousenko, I. Terentyeva and D. Bakhtiiarov, “Protected Voice Control System of UAV,” 2019 IEEE 5th International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), 2019, pp. 295-298, doi: 10.1109/APUAVD47061.2019.8943926.
R. Odarchenko, O. Lavrynenko, D. Bakhtiiarov, S. Dorozhynskyi and V. A. O. Zharova, “Empirical Wavelet Transform in Speech Signal Compression Problems,” 2021 IEEE 8th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), 2021, pp. 599-602, doi: 10.1109/PICST54195.2021.9772156.
O. Lavrynenko, R. Odarchenko, G. Konakhovych, A. Taranenko, D. Bakhtiiarov and T. Dyka, “Method of Semantic Coding of Speech Signals based on Empirical Wavelet Transform,” 2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT), 2021, pp. 18-22, doi: 10.1109/AICT52120.2021.9628985.
