Identifying optimal message embedding location in audio steganography using generative adversarial networks

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.263695

Keywords:

generative adversarial network, least significant bit matching, markov model, audio steganography, jpeg image

Abstract

Audio steganography (AS) uses the auditory redundancy of the human ear to conceal the hidden message inside the audio track. In recent studies, deep learning-based steganalysis has swiftly revealed AS by extracting high-dimensional stego acoustic features for categorization. There is still an opportunity for improvement in the current audio steganography required for managing communication confidentiality, access control and data protection. The main objective of this research is to improving the data protection by identifying the data embedding location in the audio. Generative Adversarial Network-based Audio Steganography Framework (GAN-ASF) is presented in this study, and it can automatically learn to provide better cover audio for message embedding. The suggested framework's training architecture comprises a generator, a discriminator, and a steganalyzer learned using deep learning. The Least Significant Bit Matching (LSBM) message embedding technique encrypts the secret message into the steganographic cover audio, which is then forwarded to a trained steganalyzer for misinterpretation as cover audio. After performing the training, stenographic cover audio has been generated for encoding the secret message. Here, Markov model of co-frequency sub images to generate the best cover frequency sub-image to locate an image's hidden payload. Steganographic cover audio created by GAN-ASF has been tested and found to be of excellent quality for embedding messages. The suggested method's detection accuracy is lower than that of the most current state-of-the-art deep learning-based steganalysis. This payload placement approach has considerably increased stego locations' accuracy in low frequencies. The test results GAN-ASF achieves a performance ratio of 94.5 %, accuracy ratio of 96.2 %, an error rate of 15.7 %, SNR 24.3 %, and an efficiency ratio of 94.8 % compared to other methods.

Author Biographies

Muatamed Hajer, University of Sumer

Doctor of Information Technology

Department of Computer Science

Mohammed Anbar, Universiti Sains Malaysia

Doctor of Advanced Internet Security and Monitoring

Department of National Advanced IPv6 Centre

References

  1. Zhao, J., Wang, S. (2022). A stable GAN for image steganography with multi-order feature fusion. Neural Computing and Applications. doi: https://doi.org/10.1007/s00521-022-07270-w
  2. Asimopoulos, D. C., Nitsiou, M., Lazaridis, L., Fragulis, G. F. (2022). Generative Adversarial Networks: a systematic review and applications. SHS Web of Conferences, 139, 03012. doi: https://doi.org/10.1051/shsconf/202213903012
  3. Li, X., Yao, R., Lee, J. (2022). Research on Digital Steganography and Image Synthesis Model Based on Improved Wavelet Neural Network. Computational Intelligence and Neuroscience, 2022, 1–12. doi: https://doi.org/10.1155/2022/7145387
  4. Najm, O. A., Nori, A. S. (2022). Steganography Method of the Bigger Size in WebP Image Using M2PAM Algorithm for Social Applications. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13 (2), 595–610. Available at: https://turcomat.org/index.php/turkbilmat/article/view/12359
  5. Xiang, L., Wang, R., Yang, Z., Liu, Y. (2022). Generative Linguistic Steganography: A Comprehensive Review. KSII Transactions on Internet and Information Systems, 16 (3), 986–1005. doi: https://doi.org/10.3837/tiis.2022.03.013
  6. Mallika, Ubhi, J. S., Aggarwal, A. K. (2022). Neural Style Transfer for image within images and conditional GANs for destylization. Journal of Visual Communication and Image Representation, 85, 103483. doi: https://doi.org/10.1016/j.jvcir.2022.103483
  7. Qureshi, K. N., Kaiwartya, O., Jeon, G., Piccialli, F. (2022). Neurocomputing for internet of things: Object recognition and detection strategy. Neurocomputing, 485, 263–273. doi: https://doi.org/10.1016/j.neucom.2021.04.140
  8. Phipps, A., Ouazzane, K., Vassilev, V. (2022). Securing Voice Communications Using Audio Steganography. International Journal of Computer Network and Information Security, 14 (3), 1–18. doi: https://doi.org/10.5815/ijcnis.2022.03.01
  9. Wang, Y., Huang, L., Yee, A. L. (2022). Full-convolution Siamese network algorithm under deep learning used in tracking of facial video image in newborns. The Journal of Supercomputing, 78 (12), 14343–14361. doi: https://doi.org/10.1007/s11227-022-04439-x
  10. Bao, Z., Xue, R. (2021). Survey on deep learning applications in digital image security. Optical Engineering, 60 (12). doi: https://doi.org/10.1117/1.oe.60.12.120901
  11. Zhang, D., Ma, M., Xia, L. (2022). A comprehensive review on GANs for time-series signals. Neural Computing and Applications, 34 (5), 3551–3571. doi: https://doi.org/10.1007/s00521-022-06888-0
  12. Nguyen, T. D., Le, H. Q. (2022). A secure image steganography based on modified matrix encoding using the adaptive region selection technique. Multimedia Tools and Applications, 81 (18), 25251–25281. doi: https://doi.org/10.1007/s11042-022-12677-7
  13. Hemeida, F., Alexan, W., Mamdouh, S. (2021). A Comparative Study of Audio Steganography Schemes. International Journal of Computing and Digital Systems, 10 (1), 555–562. doi: https://doi.org/10.12785/ijcds/100153
  14. Chen, L., Wang, R., Yan, D., Wang, J. (2021). Learning to Generate Steganographic Cover for Audio Steganography Using GAN. IEEE Access, 9, 88098–88107. doi: https://doi.org/10.1109/access.2021.3090445
  15. Rakshit, P., Ganguly, S., Pal, S., Le, D.-N. (2021). Securing Technique Using Pattern-Based LSB Audio Steganography and Intensity-Based Visual Cryptography. Computers, Materials & Continua, 67 (1), 1207–1224. doi: https://doi.org/10.32604/cmc.2021.014293
  16. Hameed, A. S. (2021). A High Secure Speech Transmission Using Audio Steganography and Duffing Oscillator. Wireless Personal Communications, 120 (1), 499–513. doi: https://doi.org/10.1007/s11277-021-08470-8
  17. Ying, K., Wang, R., Lin, Y., Yan, D. (2021). Adaptive Audio Steganography Based on Improved Syndrome-Trellis Codes. IEEE Access, 9, 11705–11715. doi: https://doi.org/10.1109/access.2021.3050004
  18. Abdulkadhim, H. A., Shehab, J. N. (2022). Audio steganography based on least significant bits algorithm with 4D grid multi-wing hyper-chaotic system. International Journal of Electrical and Computer Engineering (IJECE), 12 (1), 320. doi: https://doi.org/10.11591/ijece.v12i1.pp320-330
  19. Ganwani, P., Gupta, L., Jain, C., Kulkarni, R., Chaudhari, S. (2021). LSB Based Audio Steganography using RSA and ChaCha20 Encryption. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). doi: https://doi.org/10.1109/icccnt51525.2021.9580177
  20. Mahmoud, M. M., Elshoush, H. T. (2022). Enhancing LSB Using Binary Message Size Encoding for High Capacity, Transparent and Secure Audio Steganography–An Innovative Approach. IEEE Access, 10, 29954–29971. doi: https://doi.org/10.1109/access.2022.3155146
  21. Osman, O. M., Kanona, M. E. A., Hassan, M. K., Elkhair, A. A. E., Mohamed, K. S. (2022). Hybrid multistage framework for data manipulation by combining cryptography and steganography. Bulletin of Electrical Engineering and Informatics, 11 (1), 327–335. doi: https://doi.org/10.11591/eei.v11i1.3451
  22. Yang, J., Yang, Z., Zhang, S., Tu, H., Huang, Y. (2022). SeSy: Linguistic Steganalysis Framework Integrating Semantic and Syntactic Features. IEEE Signal Processing Letters, 29, 31–35. doi: https://doi.org/10.1109/lsp.2021.3122901
  23. Dhawan, S., Gupta, R. (2020). Analysis of various data security techniques of steganography: A survey. Information Security Journal: A Global Perspective, 30 (2), 63–87. doi: https://doi.org/10.1080/19393555.2020.1801911
  24. Jeyalilly, M., Kannan, S., Muthukumaravel, A. (2020). New Innovative Secure Audio Stenography Using Frequency Hopped Spread Spectrum Techniques in Mobile Computing. Computing. Malaya Journal of Matematik, S (2), 4564–4568. Available at: https://www.malayajournal.org/articles/MJM0S201177.pdf
  25. Cui, J., Zhang, P., Li, S., Zheng, L., Bao, C., Xia, J., Li, X. (2021). Multitask Identity-Aware Image Steganography via Minimax Optimization. IEEE Transactions on Image Processing, 30, 8567–8579. doi: https://doi.org/10.1109/tip.2021.3107999
  26. Multiple Voip Steganography Datasets. Available at: https://www.kaggle.com/datasets/wujunyan/amr-steg

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Published

2022-08-31

How to Cite

Hajer, M., & Anbar, M. (2022). Identifying optimal message embedding location in audio steganography using generative adversarial networks. Eastern-European Journal of Enterprise Technologies, 4(9(118), 59–68. https://doi.org/10.15587/1729-4061.2022.263695

Issue

Vol. 4 No. 9(118) (2022): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2022 Muatamed Hajer, Mohammed Anbar

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