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JOIG 2024 Vol.12(1): 10-15
doi: 10.18178/joig.12.1.10-15

Blind Steganalysis Method Using Image Spectral Density and Differential Histogram Correlative Power Spectral Density

Hafedh Ali Shabat *, Khamael Raqim Raheem, and Wafaa Mohammed Ridha Shakir
Technical Institute of Babylon, AL-Furat AL-Awsat Technical University (ATU), Kufa, Iraq
Email: h.ali@atu.edu.iq (H.A.S.); khmrakrah@atu.edu.iq (K.R.R.); inb.wfa@atu.edu.iq (W.M.R.S.)
*Corresponding author

Manuscript received July 11, 2023; revised July 25, 2023; accepted September 27, 2023; published January 4, 2024.

Abstract—Recent research has demonstrated the success of employing neural networks for the purpose of detecting image tampering. Nevertheless, the utilization of reference-free steganalysis has become increasingly popular as a result of the challenges associated with obtaining an annotated dataset. This dataset is crucial for the classification process using neural networks, which aims to detect and identify instances of tampering. This paper introduces a robust approach to blind steganalysis, utilizing image spectral density and differential histogram correlative power spectral density. The proposed method employed two distinct forms of image data, namely a gray-scale image and true-color image data. The results indicate that the proposed methodology successfully achieved the anticipated outcomes in identifying manipulated images as evidenced by its successful application on the two distinct datasets. In the experiment results, the proposed technique succeeded quite well in terms of accuracy at low embedding ratios. Also, it successfully recognized sequential and random least significant bit steganography.

Keywords—steganalysis, steganography, signal processing, entropy, spectral density

Cite: Hafedh Ali Shabat, Khamael Raqim Raheem, and Wafaa Mohammed Ridha Shakir, "Blind Steganalysis Method Using Image Spectral Density and Differential Histogram Correlative Power Spectral Density," Journal of Image and Graphics, Vol. 12, No. 1, pp. 10-15, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.