Volume 15 , Issue 1 , PP: 01-10, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Akram Mshet 1 * , Huda Tayyeh 2
Doi: https://doi.org/10.54216/JCIM.150101
Steganography involves concealing hidden messages inside various types of media, whereas steganalysis is the process of identifying the presence of steganography. Convolutional neural networks (CNN), a type of neural network that outperformed previously proposed machine learning-based methods when introduced, are among the models used for deep learning. While CNN-based methods may yield satisfactory results, they face challenges in terms of classification accuracy and network training stability. The present research introduces a CNN structure to increase hidden data detection and spatial domain image training reliability. The suggested method includes pre-processing, feature extraction, and classification. Evaluation of performance is conducted on datasets Break Our Steganographic System Base (BOSSbase-.01) and Break Our Watermarking System (BOWS2) with three adaptive steganography algorithms. Wavelet Obtained Weights (WOW), Spatial Universal Wavelet Relative Distortion (S-UNIWARD), and Highly Undetectable steGO (HUGO) operating at low payload capacities of 0.2 and 0.4 bits per pixel (bpp). The experimental results surpass the accuracy and network stability of prior publications. Training accuracy ranges from 91% to 94%, and testing accuracy ranges from 74.8% to 86.65%.
Deep learning , Steganography , Convolutional neural network , Steganalysis
[1] Darch Abed Dawar, A. (2024). Enhancing Wireless Security and Privacy: A 2-Way Identity Authentication Method for 5G Networks. International Journal of Mathematics, Statistics, and Computer Science, 2, 183–198. https://doi.org/10.59543/ijmscs.v2i.9073
[2] N. J. De La Croix, C.C.I.a.T.A., Secret message protection using fuzzy logic and difference expansion in digital images.Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), pp 1–5. NIGERCON54645.2022.9803151 IEEE 2022.
[3] Ferreira, W.D.F.C.B.R., A review of digital image forensics. leceng.2020.106685. Comput Electr Eng, 2020.
[4] Warkentin, M., E. Bekkering, and M. Schmidt, Steganography: Forensic, Security, and Legal Issues. Journal of Digital Forensics, Security and Law, 2008.
[5] Liu, J., et al., Efficient binary image steganalysis based on ensemble neural network of multi-module. Journal of Real-Time Image Processing, 2019. 17(1): p. 137-147.
[6] Ahmed A. Alsabhany, A.H.A., Digital audio steganography: Systematic review, classification, and analysis of the current state of the art. Computer Science, November 2020.
[7] Y. Qian, J.D., W. Wang, and T. Tan, ‘‘Deep learning for steganalysis via convolutional neural networks,’’ in Proc. IS T Int. Symp. Electron. Imag.(EI), vol. 9409, 2015, Art. no. 94090J.
[8] Xu, G., H.-Z. Wu, and Y.-Q. Shi, Structural Design of Convolutional Neural Networks for Steganalysis. IEEE Signal Processing Letters, 2016. 23(5): p. 708-712.
[9] Ye, J., J. Ni, and Y. Yi, Deep Learning Hierarchical Representations for Image Steganalysis. IEEE Transactions on Information Forensics and Security, 2017. 12(11): p. 2545-2557.
[10] Yedroudj, M., F. Comby, and M. Chaumont, Yedroudj-Net: An Efficient CNN for Spatial Steganalysis, in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2018. p. 2092-2096.
[11] K. He, X.Z., S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition 2016, pp. 770–778.
[12] Zhang, R., et al., Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis. IEEE Transactions on Information Forensics and Security, 2020. 15: p. 1138-1150.
[13] Reinel, T.-S., et al., GBRAS-Net: A Convolutional Neural Network Architecture for Spatial Image Steganalysis. IEEE Access, 2021. 9: p. 14340-14350.
[14] Taha Ahmed, I., B. Tareq Hammad, and N. Jamil, Image Steganalysis based on Pretrained Convolutional Neural Networks, in 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA). 2022. p. 283-286.
[15] de La Croix, N.J. and T. Ahmad, Toward Hidden Data Detection via Local Features Optimization in Spatial Domain Images, in 2023 Conference on Information Communications Technology and Society (ICTAS). 2023. p. 1-6.
[16] Agarwal, S., C. Kim, and K.-H. Jung, Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network. Applied Sciences, 2022. 12(21).
[17] Z. Yang, Q.L., S. Luo, S. Tan and B. Li,, "Improving VGG-Style Convnet for JPEG Steganalysis,". IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Republic of, 2024, pp(4450-4454).
[18] Mario Alejandro Bravo-Ortiz a, E.M.-R.a., CVTStego-Net: A convolutional vision transformer architecture for spatial image steganalysis. Journal of Information Security and Applications, 2024.
[19] P. Bas, T.F., and T. Pevný, ’Break Our Steganographic System’: The Ins and Outs of Organizing BOSS. In Proceedings of the 13th International Conference on Information Hiding IH’2011, volume 6958 of Lecture Notes in Computer Science, pages 59–70, Prague, Czech Republic May 2011. Springer.
[20] B. Li, M.W., J. Huang, and X. Li, “A new cost function for spatial image steganography,”. in 2014 IEEE international Conference on Image Processing (ICIP) Oct 2014, pp. 4206–4210.
[21] Holub, V., J. Fridrich, and T. Denemark, Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security, 2014. 2014(1).
