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A secure adaptive Hidden Markov Model-based JPEG steganography method

(J-HMMSteg)

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Abstract

This study introduces J-HMMSteg, an adaptive and secure JPEG image steganography technique designed for data embedding with minimal distortion. J-HMMSteg employed a block-wise analysis approach to detect shifts in image statistics and was performed in three phases. Firstly, it constructed statistical features of the images by analyzing intra-interblock correlations of quantized Discrete Cosine Transform (DCT) coefficients. Secondly, it utilized these features to create the Hidden Markov Model (HMM) of the images to capture complex characteristics such as smoothness, regularity, continuity, consistency, and periodicity. Finally, JHMMSteg utilized a maximum likelihood embedder driven by a threshold using Kullback-Leibler Divergence (KLD). The embedder maintained an optimal correlation by limiting the number of coefficients changed within a block according to its threshold; this ensured that only permissible distortions were introduced. This resulted in a stego image with minimal deviation from the HMM of the cover image’s statistical distribution. The experimental analysis of J-HMMSteg was conducted on a database of 85000 JPEG images against four state-of-the-art approaches; the results demonstrated that J-HMMSteg was highly imperceptible, robust against RS steganalysis, and enhanced security against ensemble steganalysis.

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Availability of data and materials

The images supporting this study’s findings consist of two disjoint sets that are freely available. The BOSSbase 1.01 is available at https://dde.binghamton.edu/download and consists of 10,000 grayscale images of size 512\(\times \)512. The ALASKA2 is available at www.kaggle.com/alaska2images and comprises 75,000 color images of size 256\(\times \)256. These 85,000 images are compressed in the JPEG domain with a quality factor of 75 and resized to 512\(\times \)512 for analysis during this study.

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Authors and Affiliations

  1. Computer Science and Engineering, Manipur Technical University, Imphal, 795001, Manipur, India

    Debina Laishram

  2. Computer Science and Engineering, National Institute of Technology, Dimapur, 797103, Nagaland, India

    Themrichon Tuithung

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  1. Debina Laishram
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  2. Themrichon Tuithung
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Correspondence to Debina Laishram.

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Laishram, D., Tuithung, T. A secure adaptive Hidden Markov Model-based JPEG steganography method. Multimed Tools Appl 83, 38883–38908 (2024). https://doi.org/10.1007/s11042-023-17152-5

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