Concealment and enhancement of signal security in the audio-optical channel based on fractional Fourier amplitude and phase recovery (G-S)

Varamini, Gohar; Mazloum, Jalil; Ghoami, Alireza

Concealment and enhancement of signal security in the audio-optical channel based on fractional Fourier amplitude and phase recovery (G-S)

Document Type : Original Article

Authors

Gohar Varamini ¹

Jalil Mazloum ²

Alireza Ghoami ³

¹ Department of Electrical Engineering, Bey. C., Islamic Azad University, Beyza, Iran

² Department of Electrical Engineering Shahid Sattari Aeronautical University of Science and Technology

³ Department of Water Engineering, Shi. C., Islamic Azad University, Shiraz, Iran

Abstract

This paper examines an audio signal encoding method based on the Grachberg-Saxton phase recovery algorithm in the fractional Fourier transform domain and presents a hybrid model for providing security and data hiding in information channels. This algorithm first encodes the original audio signal into an image format and encrypts it.
This algorithm first encodes the original audio signal into an image format and encrypts it. Subsequently, the G-S phase recovery algorithm in the fractional Fourier domain is used to extract the phase information of the audio signal. Accordingly, the original audio phase is replaced with a reference phase representing the hidden information to achieve information embedding.
This technology includes the ability to generate two different types of keys according to the encryption requirements, which increases the difficulty of decryption and ensures information security.
Finally, the encrypted image can be decrypted and restored to the original audio signal. This scheme realizes the effective embedding and extraction of hidden information in audio signals through the replacement and recovery of phase information. In this scheme, information security has been significantly increased by about 98%

Keywords

"recovery algorithm"

"Grechberg-Saxton"

"information security"

" secret information"

20.1001.1.28211553.1404.4.4.3.7

Subjects

IT and cyber warfare (encryption and data security, penetration and disruption in information networks, radars, dealing with hackers, etc.)

[1] V. Dankan Gowda, A. PolaJ. R. Hershey, Z. Chen, J. Le Roux, and S. Watanabe, “Deep clustering: discriminative embeddings for segmentation andseparation,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022

DOI: https://doi.org/10.4018/979-8-3373-0330-7.ch008

[2] Z. Wang, J. Le Roux, and J. R. Hershey, “Multi-channel Deep Clustering: Discriminative spectral and spatial embeddings for speaker-independent speech separation,” in International Conference

on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023.

[3] Akhtar, M.S., Ekbal, A., Cambria, E.: How intense are you? predicting intensities of emotions and entiments using stacked ensemble [application notes]. IEEE Computational Intelligence Magazine 15(1), 64–75 (2020)

[4] M. Alajeely, R. Doss, and A. Ahmad, “Routing Protocols in Opportunistic Networks – A Survey,” IETE Tech. Rev., vol. 35, no. 4, pp. 369–387, 2018.

DOI: https://doi.org/10.1080/02564602.2017.1304834

[5] K. Ahmad, M. Fathima, M. S. Hossen, J. Ahamed, and K. A. Bin Ahmad, “Opportunistic Networks: An Empirical Research of Routing Protocols and Mobility Models,” SN Comput. Sci., vol. 4, no. 5, p. 652, 2023.

DOI: https://doi.org/10.1007/s42979-023-02054-y

[6] Alhussan, A., M. Talaat, F., El-kenawy, E.S., Abdelhamid, A., Ibrahim, A., Khafaga, D., Alnaggar, M.: Facial expression recognition model depending on optimized support vector machine. Computers, Materials and Continua 76, 499–515 (06 2023).

https://doi.org/10.32604/cmc.2023.039368

[7] R. Dalal, M. Khari, J. P. Anzola, and V. Garcia-Diaz, “Proliferation of Opportunistic Routing: A Systematic Review,” IEEE Access, vol. 10, pp. 5855–5883, 2022.

DOI: https://doi.org/10.1109/ACCESS.2021.3136927

[8] Li, H., Wang, N., Yang, X., Wang, X., Gao, X.: Towards semi-supervised deep facial expression recognition with an adaptive confidence margin. 2022 IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR) pp. 4156–4165 (2022), https://api.semanticscholar.org/CorpusID:247618790

[9] Li, S., Deng, W.: Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition. IEEE Transactions on Image Processing 28(1), 356–370 (2019). https://doi.org/10.1109/TIP.2018.2868382

[10] Liu, F.: Artificial intelligence in emotion quantification : A prospective verview. CAAI Artificial Intelligence Research 3, 9150040 (2024). https://doi.org/10.26599/AIR.2024.9150040, https://www.sciopen.com/article/10.

26599/AIR.2024.9150040

[11] S. Esmaeili and J. Ghasemi, “A location-aware covert communication protocol in inter-environmental communication applications,” Alexandria Eng. J., vol. 123, pp. 592–609, 2025.

DOI: https://doi.org/10.1016/j.aej.2025.03.087

[12] Liu, F., Wang, H.Y., Shen, S.Y., Jia, X., Hu, J.Y., Zhang, J.H., Wang, X.Y., Lei,Y., Zhou, A.M., Qi, J.Y., Li, Z.B.: Opo-fcm: A computational affection based occpad-ocean federation cognitive modeling approach. IEEE Transactions on Computational Social Systems 10(4), 1813–1825 (2023). https://doi.org/10.1109/

TCSS.2022.3199119

[13] E. H. Houssein, M. R. Saad, Y. Djenouri, G. Hu, A. A. Ali, and H. Shaban, “Metaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challenges,” Cluster Comput., vol. 27, no. 10, pp. 13643–13673, 2024.

DOI: https://doi.org/10.1007/s10586-024-04619-9

[14] D. Bahrepour, N. Evaznia, and T. Khodabakhshi, “A New Resource Allocation Method Based on PSO in Cloud Computing,” Int. J. Web Res., vol. 7, no. 2, pp. 13–21, 2024.

DOI: https://doi.org/10.22133/ijwr.2024.457539.1216

[15] Liu, H., Cai, H., Lin, Q., Li, X., Xiao, H.: Adaptive multilayer perceptual attention network for facial expression recognition. IEEE Transactions on Circuits and Systems for Video Technology 32(9), 6253–6266 (2022)

[16] N. Evaznia, R. Ebrahimi, and D. Bahrepour, “An Energy-Aware Approach to Virtual Machine Consolidation Using Classification and the Dragonfly Algorithm in Cloud Data Centers,” J. Inf. Syst. Telecommun., vol. 12, no. 48, pp. 280–290, 2025.

DOI: https://doi.org/10.61186/jist.48021.12.48.280

[17] Ngwe, J.L., Lim, K.M., Lee, C.P., Ong, T.S., Alqahtani, A.: Patt-lite: Lightweight patch and attention mobilenet for challenging facial expression recognition. IEEE Access 12, 79327–79341 (2024). https://doi.org/10.1109/ACCESS.2024.3407108

[18] S. Chaurasia and K. Kumar, “EEMOR: Energy Eflicient Metaheuristic Opportunistic Routing Protocol for WSNs.,” Adhoc Sens. Wirel. Networks, vol. 55, 2023.

DOI: https://doi.org/10.32908/ahswn.v55.9265

[19] M. Sharifi Sani, S. Iranmanesh, H. Salarian, F. Tubbal, and R. Raad, “Optimizing Energy Efficiency in Opportunistic Networks: A Heuristic Approach to Adaptive Cluster-Based Routing Protocol,” Information, vol. 15, no. 5, p. 283, 2024.

DOI: https://doi.org/10.3390/info15050283

[20] Y. Zing and N. Zhao, “Routing revolution: strategic applications of meta-heuristic AI in wireless sensor networks—a comprehensive survey,” Multimed. Tools Appl., vol. 84, no. 35, pp. 44605–44646, 2025.

DOI: https://doi.org/10.1007/s11042-025-20843-w

[21] S. Chaurasia, K. Kumar, and A. K. Kamboj, “EHRP-WSN: Energy-Efficient Hyperheuristic Routing Protocol for Wireless Sensor Networks,” AEU - Int. J. Electron. Commun., vol. 202, p. 156044, 2025.

DOI: https://doi.org/10.1016/j.aeue.2025.156044

[22] J. M. Belman-Flores, D. A. Rodríguez-Valderrama, S. Ledesma, J. J. García-Pabón, D. Hernández, and D. M. Pardo-Cely, “A Review on Applications of Fuzzy Logic Control for Refrigeration Systems,” Appl. Sci., vol. 12, no. 3, p. 1302, 2022.

DOI: https://doi.org/10.3390/app12031302

[23] Qian, B., Chen, H., Xu, Y., Wen, Y., Li, H., Xie, Y., Feng, D.D., Kim, J., Bi, L.,Xu, X., He, X., Sheng, B.: Deep contour attention learning for scleral deformation from oct images. The Visual Computer pp. 1–16 (2024)

[24] She, J., Hu, Y., Shi, H., Wang, J., Shen, Q., Mei, T.: Dive into ambiguity: Latent distribution mining and pairwise uncertainty estimation for facial expression recognition. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6244–6253 (2021). https://doi.org/10.1109/CVPR46437.2021.00618

[25]Khalilpour, J., Moradi, S. H., & Zarezadeh, I. (2023). Design and fabrication of Fabry-Perot fiber optic sensor with FMCW phase extraction method for acoustic detection implementation. Journal of Acoustical Engineering Society of Iran, 10(2) http://joasi.ir/article-1-238-fa.html

[26] M. F. Khan, E. A. Felemban, S. Qaisar, and S. Ali, “Performance Analysis on Packet Delivery Ratio and End-to-End Delay of Different Network Topologies in Wireless Sensor Networks (WSNs),” in 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, IEEE, pp. 324–329, 2013.

DOI: https://doi.org/10.1109/MSN.2013.74