Current Volume 9
A Comprehensive Survey of Deep Learning-Based Image Steganalysis: From Statistical Methods to Adversarial Robustness
Abstract
This survey presents an overview of the development of image steganalysis from basic statistical models to the latest deep learning architectures. A total of 24 significant studies were analyzed, which are grouped into six important dimensions: embedding domain coverage, dataset diversity, low-payload sensitivity, adversarial robustness, pixel-level defense evaluation, computational efficiency. It also shows that the literature is fragmented, with no existing model that effectively covers the cases of heterogeneous dual-domain detection, realistic adversarial robustness on pixel level, and lightweight deployment. Three main gaps were identified: cover-source mismatch in the case of homogeneous training data, single domain architectural limitations, feature space adversarial evaluation that does not represent actual threat models; a unified taxonomy for future research is proposed. This survey provides a well-founded base for quantifying dataset heterogeneity from an information theoretic perspective, providing a principled ground for assessing generalisability in steganalysis systems.
Keywords
Adversarial Robustness, Dual-Domain Detection, Cover-Source Mismatch, Steganalysis.
Citations
IRE Journals:
Shehu Asmau, Kirubakaran Prema, Muhammad L. Bilkisu "A Comprehensive Survey of Deep Learning-Based Image Steganalysis: From Statistical Methods to Adversarial Robustness" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 1372-1383 https://doi.org/10.64388/IREV9I12-1718767
IEEE:
Shehu Asmau, Kirubakaran Prema, Muhammad L. Bilkisu
"A Comprehensive Survey of Deep Learning-Based Image Steganalysis: From Statistical Methods to Adversarial Robustness" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1718767
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