Document Type

Article

Publication Date

2025

DOI

10.3390/electronics14224490

Publication Title

Electronics

Volume

14

Issue

22

Pages

4490

Abstract

With the increasing sophistication of Artificial Intelligence (AI), traditional digital steganography methods face a growing risk of being detected and compromised. Adversarial attacks, in particular, pose a significant threat to the security and robustness of hidden information. To address these challenges, this paper proposes a novel AI-based steganography framework designed to enhance the security of concealed messages within digital images. Our approach introduces a multi-stage embedding process that utilizes a sequence of encoder models, including a base encoder, a residual encoder, and a dense encoder, to create a more complex and secure hiding environment. To further improve robustness, we integrate Wavelet Transforms with various deep learning architectures, namely Convolutional Neural Networks (CNNs), Bayesian Neural Networks (BNNs), and Graph Convolutional Networks (GCNs). We conducted a comprehensive set of experiments on the FashionMNIST and MNIST datasets to evaluate our framework’s performance against several adversarial attacks. The results demonstrate that our multi-stage approach significantly enhances resilience. Notably, while CNN architectures provide the highest baseline accuracy, BNNs exhibit superior intrinsic robustness against gradient-based attacks. For instance, under the Fast Gradient Sign Method (FGSM) attack on the MNIST dataset, our BNN-based models maintained an accuracy of over 98%, whereas the performance of comparable CNN models dropped sharply to between 10% and 18%. This research provides a robust and effective method for developing next-generation secure steganography systems.

Rights

© 2025 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The MNIST and FashionMNIST datasets used in this study are publicly available. They can be downloaded from Kaggle at https://www.kaggle.com/datasets/hojjatk/mnist-dataset, (accessed on 14 November 2025), and https://www.kaggle.com/datasets/zalando-research/fashionmnist, (accessed on 14 November 2025), respectively."

Original Publication Citation

Huynh, N. D., Jiang, J., Chen, C.-H., & Yang, W. C. (2025). AI-based steganography method to enhance the information security of hidden messages in digital images. Electronics, 14(22), Article 4490. https://doi.org/10.3390/electronics14224490

ORCID

0009-0008-1426-6156 (Huynh), 0000-0003-2958-5666 (Jiang), 0000-0002-4860-9187 (Chen),

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