Steganography Information Retrieval Mechanism Using Deep Neural Network

  • Hasan Kareem Abdulrahman

Abstract

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.

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References

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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. https://doi.org/10.33130/AJCT.2022v08i01.022