Image steganography – classic and promising methods: a study

Abstract

Steganography, the art and science of hiding information within digital media, remains a dynamic and increasingly vital discipline in the age of pervasive  digital communication and cybersecurity threats. Images, in particular, serve as highly adaptable carriers for covert data due to their ubiquity and rich  payload capacity. This paper presents a comprehensive classification of image-based steganographic techniques, surveying both time-tested methods (e.g.,  LSB modification, wavelet transform) and cutting-edge approaches. We highlight how artificial intelligence—through deep learning models, generative adversarial networks, and AI-driven compression/enhancement—can greatly improve embedding robustness and evasion of detection. Furthermore, we explore the nascent frontier of quantum steganography, leveraging superposition, entanglement, and quantum key distribution to achieve unprecedented levels of security. Finally, we outline promising research directions that fuse classical methods with next-generation AI and quantum technologies, setting the agenda for the next wave of advances in secure information hiding.