Machine learning–based steganography embeds secret messages into digital media using data-driven models that learn optimal representations to conceal information while preserving visual quality. Unlike classical methods that rely on predetermined heuristics, machine learning techniques optimize embedding strategies automatically through training, adapting to diverse content and attack scenarios. This paper investigates the theoretical foundations, model architectures, embedding and extraction algorithms, evaluation metrics, and robustness properties of machine learning-based steganographic frameworks. We propose and evaluate a Generative Adversarial Network (GAN)-inspired model specially customized for image steganography, to establish its performance on benchmark datasets, and analyse their compromises between capacity, invisibility, and hardiness to steganalysis. Experimental results indicate that the proposed approach achieves competitive imperceptibility and message extraction accuracy while resisting common detection methods. We conclude with a discussion of challenges and future directions in machine learning-based secure hiding.

Machine learning–based steganography represents an effective and adaptable method for hiding information within digital media. By leveraging neural networks trained end-to-end, the proposed framework achieves high visual quality, accurate message extraction, and competitive resistance to detection. This research contributes a practical model and evaluation insights, highlighting both capabilities and limitations. Continued advancements in learning strategies, model design, and robust optimization are expected to further enhance secure covert communication.

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