News

 Statistics

Number of Journals34
Number of Issues1,306
Number of Articles9,427
Article View9,188,648
PDF Download5,620,937
Reza, ., Matinfar, A. (2023). Steganography Audio Based on Zero-Tree Wavelet Transform Algorithm. Electronic and Cyber Defense, 14(3), 115-130.
ٍEsfahani Reza; A. R. Matinfar. "Steganography Audio Based on Zero-Tree Wavelet Transform Algorithm". Electronic and Cyber Defense, 14, 3, 2023, 115-130.
Reza, ., Matinfar, A. (2023). 'Steganography Audio Based on Zero-Tree Wavelet Transform Algorithm', Electronic and Cyber Defense, 14(3), pp. 115-130.
Reza, ., Matinfar, A. Steganography Audio Based on Zero-Tree Wavelet Transform Algorithm. Electronic and Cyber Defense, 2023; 14(3): 115-130.

Steganography Audio Based on Zero-Tree Wavelet Transform Algorithm

پدافند غیرعامل
Volume 14, Issue 3 - Serial Number 55, September 2023, Pages 115-130    PDF (1.29 M)
Document Type: tarvigi
Authors
ٍEsfahani Reza* 1; A. R. Matinfar2
1Scientific Department of Communication
2ihu
Receive Date: 28 March 2023,  Revise Date: 06 June 2023,  Accept Date: 07 August 2023 
Abstract
Audio steganography is very important, equally steganography in other media (image, video, etc.). In this paper is presented steganography of audio based on embedded zero-tree waveleten transform algorithm. Improving the resistance against white noise and additive noise with the lowest SNR is one of the important topics in steganography. The proposed algorithm is more robust against normal white noise than uniform noise and has a bit error rate of less than 1 bit against SNRs higher than 10db. According to the obtained BER, if the proposed algorithm is attacked, the hidden signal is lost completel. Also, the proposed method is resistant against additive noise. The proposed algorithm has the least changes in the sound smoothness criterion in frequency domain with Capstrom distance scale and audio files in the form of music with soft tone (loudness), and the increase of secret message does not have much effect on creating disturbances in the frequency domain. The proposed algorithm of the frequency spectrum does not change the audio signal much, and it also follows the property of the hearing threshold level, and high-pitched music with male speech has the best results, so it is favorable to the spectrum structure of Bark. Also, the proposed algorithm has favorable results in the time domain. The lowest SNR is related to high-pitched music with female speech, which has an SNR of about 13db. According to the obtained results, we will have the worst case of embedding a secret message by choosing the audio signal with female speech. Because there is a certain smoothness in the fmale speech signal. Therefore, this uniform state will be lost to some extent by embedding a secret message in this type of audio signal, and the CZD criterion will increase according to the component-by-component comparison of the two main signals and the signal containing the secret message.
 
 
 
Keywords
Embedded Zero Tree Wavelet Transform (EZW); White Noise; Additive Error; BSD and MBSD Criteria; CDM Kapstrom Distance Scale; ISD and COSH Distance; Short Time Radon Transform STFRT; SNR and CZD
References

[1] C. Kratzer, J. Dittmann, T. Vogel and R. Hillert, "Design and evaluation of steganography for voice-over-IP," 2006 IEEE International Symposium on Circuits and Systems, Kos, Greece, p. 4, 2006.                                    doi: 10.1109/ISCAS.2006.1693105.

[2] L. Boney, A. H. Tewfik, and K. N. Hamdy, "Digital watermarks for audio signals," Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems, Hiroshima, Japan, pp. 473-480,1996.         doi: 10.1109/MMCS.1996.535015.

[3] Cvejic, Nedeljko, “Algorithms For Audio Watermarking  and Steganography, 2004.

[4] S. S. Agaian, D. Akopian, O. Caglayan, and S. A. D'Souza, "Lossless Adaptive Digital Audio Steganography," Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005., Pacific Grove, CA, USA, pp. 903-906, 2005. doi: 10.1109/ACSSC.2005.1599886.

[5] W. C. CHU, “Speech Coding Algorithms-Foundation and Evaluation of Standatadized Coder, 2003.

[6] J. Herre and S. Dick, “Psychoacoustic Models for Perceptual Audio Coding—A Tutorial Review,” Applied Sciences, vol. 9, no. 14, p. 2854, Jul. 2019, doi: 10.3390/app9142854.

[7] J. M. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients," in IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3445-3462, Dec. 1993, doi: 10.1109/78.258085.

[8] V. Ralph Algazi and Robert R. Estes Jr. "Analysis-based coding of image transform and subband coefficients", Proc. SPIE 2564, Applications of Digital Image Processing XVIII, 22 August 1995.

[9] K. L. Narasihimhaprasad, M. V. Nagabhushanam, V. V. Satyanarayana Tallapragada, and J. Krishna Sunkara, "Embedded Zero-Tree Wavelet Coding with Selective Decomposition Bands," 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 0445-0449, 2019. doi: 10.1109/ICCSP.2019.8698062.

[10] J. Antonio Alvarez-Cedillo, T. Alvarez-Sanchez, M. Aguilar-Fernandez, and J. Sandoval-Gutierrez, ‘Many-Core Algorithm of the Embedded Zerotree Wavelet Encoder’, Coding Theory. IntechOpen, Mar. 11, 2020. doi: 10.5772/intechopen.89300.

[11] Rekik, Siwar et al. “Speech steganography using wavelet and Fourier transforms.” EURASIP Journal on Audio, Speech, and Music Processing, pp. 1-14, 2012.

[12] H. Inoue, A. Miyazaki, A. Yamamoto, and T. Katsura, "A digital watermark based on the wavelet transform and its robustness on image compression," Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), Chicago, IL, USA, vol. 2, pp.     391-395, 1998. doi: 10.1109/ICIP.1998.723388.

[13] Z. Xu, K. Wang, and X. -h. Qiao, "Digital Audio Watermarking Algorithm Based on Quantizing Coefficients," 2006 International Conference on Intelligent Information Hiding and Multimedia, Pasadena, CA, USA, 2006, pp. 41-46, doi: 10.1109/IIH-MSP.2006.265115.

[14] Xiang, Shijun, Hyoung Joong Kim, and Jiwu Huang. "Audio watermarking robust against time-scale modification and MP3 compression." Signal Processing 88.10, pp. 2372-2387, 2008.

[15] M. Baziyad, et al., "Maximizing embedding capacity for speech steganography: a segment-growing approach," Multimedia Tools and Applications, vol. 80, pp. 24469-24490, 2021.

[16] F. J. Farsana and K. Gopakumar, "Speech encryption algorithm based on nonorthogonal quantum state with hyperchaotic keystreams." Advances in Mathematical Physics 2020, pp. 1-12, 2020.

[17] A. Kuznetsov, et al., "Direct Spread Spectrum Technology for Data Hiding in Audio,." Sensors 22.9, 3115, 2022.

Statistics
Article View: 3,152
PDF Download: 1,502