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Beyond traditional steganography: enhancing security and performance with spread spectrum image steganography

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Abstract

This study investigates the innovative application of Direct Sequence Spread Spectrum (DSSS) technology in the realm of image steganography, known as Spread Spectrum Image Steganography (SSIS). By interpreting the cover image as noise in the communication channel, SSIS capitalizes on the noise-resistant properties of broadband communication systems to effectively conceal information within images. We focus on the development of new classes of spreading sequences with desirable ensemble and correlation properties, which significantly impact the performance of SSIS. We propose a data hiding method that directly addresses spreading sequences, resulting in minimized cover image distortion and heightened resistance to message detection. Furthermore, we explore adaptive spreading sequences that consider the statistical properties of the cover image, substantially reducing error intensity in recovered messages and improving the overall steganographic system performance. Our experiments confirm the advantages of the proposed system and support the theoretical arguments. In addition, we employ artificial neural networks for steganalysis, generating several datasets with varying SSIS payloads and examining the detectability of embedded data using a specially designed convolutional neural network (CNN). While this model demonstrates high effectiveness on other datasets, the detection error probability for SSIS is considerably higher, indicating greater reliability and security even when advanced steganalysis techniques are employed. The findings highlight the potential of SSIS in developing robust and secure communication systems capable of functioning effectively in high-noise environments while preserving the integrity of the cover image.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

• This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 101007820—TRUST. This publication reflects only the author’s view and the REA is not responsible for any use that may be made of the information it contains.

• This research was funded by the European Union – NextGenerationEU under the Italian Ministry of University and Research (MIUR), National Innovation Ecosystem grant ECS00000041-VITALITY-CUP D83C22000710005.

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Author notes

  1. Oleksandr Kuznetsov, Emanuele Frontoni and Kyrylo Chernov contributed equally to this work.

Authors and Affiliations

  1. Department of Political Sciences, Communication and International Relations, University of Macerata, Via Crescimbeni, 30/32, 62100, Macerata, Italy

    Oleksandr Kuznetsov & Emanuele Frontoni

  2. Department of Information and Communication Systems Security, School of Computer Sciences, V. N. Karazin Kharkiv National University, 4 Svobody Sq., Kharkiv, 61022, Ukraine

    Oleksandr Kuznetsov & Kyrylo Chernov

  3. Department of Information Engineering, Marche Polytechnic University, Via Brecce Bianche 12, 60131, Ancona, Italy

    Emanuele Frontoni

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Kuznetsov, O., Frontoni, E. & Chernov, K. Beyond traditional steganography: enhancing security and performance with spread spectrum image steganography. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05415-z

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