Steganography Information Retrieval Mechanism Using Deep Neural Network

  • Hasan Kareem Abdulrahman


Steganography has captivated the interest of a rising number of academics in recent years, as its applications have grown beyond information security. The most common approach is digital signal processing (DSP), which includes least significant bit (LSB) encoding. Deep learning has lately been used in various innovative approaches to the steganography problem. The bulk of existing methods, on the other hand, are designed for image in picture steganography. This study proposes using deep learning algorithms to disguise clandestine audio in digital photos. The first network conceals the concealed audio in a picture, while the second network decodes the image to retrieve the actual audio. In-depth research make use of a set of 24K images and the VIVOS Corpus audio dataset1. Experimental data shows that our strategy is more effective than earlier methods. Both the visual and audio integrity are preserved, and the maximum length of the concealed audio is substantially extended.

Keywords: Image , FFNN, FFT, WLT, RGB, Steganography.


Download data is not yet available.


[1] Shumeet Baluja. Hiding images in plain sight: Deep steganography. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vish- wanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 2069–2079. Curran Associates, Inc., 2017.
[2] Abbas Cheddad, Joan Condell, Kevin Curran, and Paul Mc Kevitt. Digital image steganography: Survey and analysis of current methods. Signal Processing, 90(3):727 – 752, 2010.
[3] Nedeljko Cvejic and Tapio Seppanen. A wavelet domain lsb insertion algorithm for high capacity audio steganography. In Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop., pages 53–55. IEEE, 2002.
[4] Nedeljko Cvejic and Tapio Seppanen. Increasing robustness of lsb audio steganography using a novel embedding method. In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004., volume 2, pages 533–537. IEEE, 2004.
[5] Kaliappan Gopalan. Audio steganography using bit modification. In 2003 International Conference on Multimedia and Expo. ICME’03. Proceedings (Cat. No. 03TH8698), volume 1, pages I–629. IEEE, 2003.
[6] Daniel W. Griffin and Jae S. Lim. Signal estimation from modified short-time fourier transform. 1984.
[7] Julio C Hernandez-Castro, Ignacio Blasco-Lopez, Juan M Estevez- Tapiador, and Arturo Ribagorda-Garnacho. Steganography in games: A general methodology and its application to the game of go. computers & security, 25(1):64–71, 2006.
[8] Chet Hosmer. Discovering hidden evidence. Journal of Digital Forensic Practice, 1(1):47–56, 2006.
[9] Nitin Kaul and Nikesh Bajaj. Audio in image steganography based on wavelet transform. International Journal of Computer Applications, 79(3), 2013.
[10] Imran Khan, Bhupendra Verma, Vijay K Chaudhari, and Ilyas Khan. Neural network based steganography algorithm for still images. In INTERACT-2010, pages 46–51. IEEE, 2010.
[11] Charles J. Kowalski. On the effects of non-normality on the distribution of the sample product-moment correlation coefficient. Journal of the Royal Statistical Society. Series C (Applied Statistics), 21(1):1–12, 1972.
[12] Charles Kurak and John McHugh. A cautionary note on image downgrading. In [1992] Proceedings Eighth Annual Computer Security Application Conference, pages 153–159. IEEE, 1992.
[13] Bin Li, Junhui He, Jiwu Huang, and Yun Qing Shi. A survey on image steganography and steganalysis. Journal of Information Hiding and Multimedia Signal Processing, 2(2):142–172, 2011.
[14] S. Hamid Nawab and Thomas F. Quatieri. Advanced topics in signal processing. chapter Short-time Fourier Transform, pages 289–337. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1987.
[15] Rully Adrian Santosa and Paul Bao. Audio-to-image wavelet transform based audio steganography. In 47th International Symposium ELMAR, 2005., pages 209–212. IEEE, 2005.
[16] STUDENT. PROBABLE ERROR OF A CORRELATION COEFFI- CIENT. Biometrika, 6(2-3):302–310, 09 1908.
[17] Dengpan Ye, Shunzhi Jiang, and Jiaqin Huang. Heard more than heard: An audio steganography method based on gan. arXiv preprint arXiv:1907.04986, 2019.
[18] Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei. Hidden: Hiding data with deep networks. In Proceedings of the European Conference on Computer Vision (ECCV), pages 657–672, 2018.
0 Views | 0 Downloads
How to Cite
Abdulrahman, H. K. (2022). Steganography Information Retrieval Mechanism Using Deep Neural Network. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 8(1), 144-148. Retrieved from