Journal of Cybersecurity and Information Management

Journal DOI

https://doi.org/10.54216/JCIM

Submit Your Paper

2690-6775ISSN (Online) 2769-7851ISSN (Print)

Volume 15 , Issue 2 , PP: 177-194, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Coverless Image Steganography Based on Machine Learning Techniques

Teba Hassan AlHamdani 1 , Suhad A. Ali 2 , Majid Jabbar Jawad 3

  • 1 Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq - (teba.alhamadani.gsci145@student.uobabylon.edu.iq)
  • 2 Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq - (wsci.suhad.ahmed@uobabylon.edu.iq)
  • 3 Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq - (wsci.majid.jabbar@uobabylon.edu.iq)
  • Doi: https://doi.org/10.54216/JCIM.150214

    Received: May 15, 2024 Revised: July 19, 2024 Accepted: October 30, 2024
    Abstract

    Image steganography is a technique used to conceal secret information within digital images in such a way that the existence of the hidden data is not perceptible to the human eye. This method leverages the vast amount of data contained in image files, embedding the secret message by altering certain pixel values in a manner that is undetectable. The primary goal of image steganography is to ensure that the embedded information is secure and invisible, maintaining the original image's appearance and quality. Applications of image steganography include secure communication, digital watermarking, and copyright protection. Advanced methods often employ complex algorithms and machine learning models to enhance the robustness and imperceptibility of the hidden data, making it resistant to detection and manipulation.. The main idea of the proposed work is to utilize features extracted from images to construct a Hash Table, which will be employed for concealing and revealing a secret message. Since the same CNN model and input image (i.e., cover image) produce identical features, even if the cover image is slightly affected by noise, the same features (and consequently the same Hash Table) will be generated. The work demonstrated promising results in regenerating images when the cover image is slightly affected. However, as the noise level increases on the cover image, the regenerated images begin to lose more details.

    Keywords :

    Image steganography , Coverless , Deep learning , Encryption , Watermarking

    References

    [1] M. Hussain, A. Abdul Wahab, N. Javed, and K.-H. Jung, “Hybrid Data Hiding Scheme Using Right-Most Digit Replacement and Adaptive Least Significant Bit for Digital Images,” Symmetry (Basel), vol. 8, no. 6, p. 41, May 2016, doi: 10.3390/sym8060041.

    [2] Q. Liu, X. Xiang, J. Qin, Y. Tan, J. Tan, and Y. Luo, “Coverless steganography based on image retrieval of DenseNet features and DWT sequence mapping,” Knowl Based Syst, vol. 192, p. 105375, Mar. 2020, doi: 10.1016/j.knosys.2019.105375.

    [3] L. Tan, J. Liu, Y. Zhou, and R. Chen, “Coverless Steganography Based on Low Similarity Feature Selection in DCT Domain,” Radioengineering, vol. 32, no. 4, pp. 603–615, Dec. 2023, doi: 10.13164/re.2023.0603.

    [4] X. Liu, Z. Li, J. Ma, W. Zhang, J. Zhang, and Y. Ding, “Robust coverless steganography using limited mapping images,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 4472–4482, Jul. 2022, doi: 10.1016/j.jksuci.2022.05.012.

    [5] Q. Liu, X. Xiang, J. Qin, Y. Tan, and Y. Qiu, “Coverless image steganography based on DenseNet feature mapping,” EURASIP J Image Video Process, vol. 2020, no. 1, p. 39, Dec. 2020, doi: 10.1186/s13640-020-00521-7.

    [6] L. Meng, X. Jiang, Z. Zhang, Z. Li, and T. Sun, “A Robust Coverless Image Steganography Based on an End-to-End Hash Generation Model,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 7, pp. 3542–3558, Jul. 2023, doi: 10.1109/TCSVT.2022.3232790.

    [7] N. A. Karim, S. A. Ali, and M. J. Jawad, “A coverless image steganography based on robust image wavelet hashing,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 20, no. 6, p. 1317, Dec. 2022, doi: 10.12928/telkomnika.v20i6.23596.

    [8] Y. Luo, J. Qin, X. Xiang, Y. Tan, Q. Liu, and L. Xiang, “Coverless real-time image information hiding based on image block matching and dense convolutional network,” J Real Time Image Process, vol. 17, no. 1, pp. 125–135, Feb. 2020, doi: 10.1007/s11554-019-00917-3.

    [9] X. Duan, H. Song, C. Qin, and M. K. Khan, “Coverless Steganography for Digital Images Based on a Generative Model,” Computers, Materials & Continua, vol. 55, no. 3, pp. 483–493, 2018.

    [10] Z. Zhou, H. Sun, R. Harit, X. Chen, and X. Sun, “Coverless Image Steganography Without Embedding,” 2015, pp. 123–132. doi: 10.1007/978-3-319-27051-7_11.

    [11] Z. Zhou, Y. Cao, X. Sun, and C. Gao, “Coverless Information Hiding Based on the Molecular Structure Images of Material,” Computers, Materials & Continua, vol. 54, no. 2, 2018.

    [12] X. Zhang, F. Peng, and M. Long, “Robust Coverless Image Steganography Based on DCT and LDA Topic Classification,” IEEE Trans Multimedia, vol. 20, no. 12, pp. 3223–3238, Dec. 2018, doi: 10.1109/TMM.2018.2838334.

    [13] A. Qiu, X. Chen, X. Sun, S. Wang, and G. Wei, “Coverless Image Steganography Method Based on Feature Selection,” Journal of Information Hiding and Privacy Protection, vol. 1, no. 2, pp. 49–60, 2019, doi: 10.32604/jihpp.2019.05881.

    [14] L. Yang, H. Deng, and X. Dang, “A Novel Coverless Information Hiding Method Based on the Most Significant Bit of the Cover Image,” IEEE Access, vol. 8, pp. 108579–108591, 2020, doi: 10.1109/ACCESS.2020.3000993.

    [15] J.-S. Pan, X.-X. Sun, H. Yang, V. Snášel, and S.-C. Chu, “Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach,” Symmetry (Basel), vol. 14, no. 2, p. 315, Feb. 2022, doi: 10.3390/sym14020315.

    [16] K. Anggriani, S.-F. Chiou, N.-I. Wu, and M.-S. Hwang, “A High-Capacity Coverless Information Hiding Based on the Lowest and Highest Image Fragments,” Electronics (Basel), vol. 12, no. 2, p. 395, Jan. 2023, doi: 10.3390/electronics12020395.

     

    Cite This Article As :
    Hassan, Teba. , A., Suhad. , Jabbar, Majid. Coverless Image Steganography Based on Machine Learning Techniques. Journal of Cybersecurity and Information Management, vol. , no. , 2025, pp. 177-194. DOI: https://doi.org/10.54216/JCIM.150214
    Hassan, T. A., S. Jabbar, M. (2025). Coverless Image Steganography Based on Machine Learning Techniques. Journal of Cybersecurity and Information Management, (), 177-194. DOI: https://doi.org/10.54216/JCIM.150214
    Hassan, Teba. A., Suhad. Jabbar, Majid. Coverless Image Steganography Based on Machine Learning Techniques. Journal of Cybersecurity and Information Management , no. (2025): 177-194. DOI: https://doi.org/10.54216/JCIM.150214
    Hassan, T. , A., S. , Jabbar, M. (2025) . Coverless Image Steganography Based on Machine Learning Techniques. Journal of Cybersecurity and Information Management , () , 177-194 . DOI: https://doi.org/10.54216/JCIM.150214
    Hassan T. , A. S. , Jabbar M. [2025]. Coverless Image Steganography Based on Machine Learning Techniques. Journal of Cybersecurity and Information Management. (): 177-194. DOI: https://doi.org/10.54216/JCIM.150214
    Hassan, T. A., S. Jabbar, M. "Coverless Image Steganography Based on Machine Learning Techniques," Journal of Cybersecurity and Information Management, vol. , no. , pp. 177-194, 2025. DOI: https://doi.org/10.54216/JCIM.150214