A comprehensive review of traditional and deep learning based techniques for digital image steganography

Digital image steganography has evolved from traditional rule-based techniques to advanced data-driven frameworks enabled by deep learning. However, existing surveys remain fragmented, often focusing on limited aspects while overlooking emerging paradigms such as blockchain-integrated and quantum-based approaches. This paper presents a comprehensive and systematic review of digital image steganography following the PRISMA 2020 guidelines, covering studies published between January 2015 and April 2026 across six major scientific databases. From an initial pool of 26,539 records, 83 relevant studies were selected through a rigorous two-stage screening process. The review provides a unified analysis of steganographic techniques by examining five dimensions: structural evolution and taxonomy, algorithmic modifications and hybridisation, application domain mapping, integration of emerging technologies, and future research trends. Comparative evaluation indicates that deep learning-based methods achieve 18–23% higher steganalysis resistance than classical approaches, whereas classical methods retain a 5–8 dB PSNR advantage. The quantitative synthesis further confirms the inherent capacity–imperceptibility–security trilemma, wherein no reviewed technique simultaneously achieves $$\text {PSNR} > 42$$ dB, embedding capacity $$> 4$$ bpp, and detection error rate $$> 0.48$$ . Six open challenges and seven future research directions are identified and grounded in evidence from the included studies, with explainable steganography, quantum-resistant frameworks, and latent diffusion model integration emerging as the most critical priorities for advancing the field toward practical and secure deployment.