Neural Steganography and Steganalysis: A Comprehensive Review on the Future of Hidden Communication

Abstract

As the digital landscape continues to expand, the secure concealment and retrieval of information have become equally critical for ensuring confidentiality and integrity. Steganography and steganalysis serve as a cornerstone for securing and retrieving sensitive information and facilitating covert communication, while effectively mitigating potential threats. Despite significant advancements in digital communication technologies, ensuring information security remains an ongoing challenge, particularly in the context of neural network (NN)‐based steganography and steganalysis architectures. This study presents a comprehensive analysis of steganography and steganalysis methods, evolving from classical to advanced intelligence frameworks by emphasizing image quality, security of hidden information, payload capacity, and robust detection mechanisms against adversarial perturbations. By leveraging state‐of‐the‐art methods such as convolutional neural network (CNN), generative adversarial network (GAN), autoencoders, and fuzzy logic (FL) for security, imperceptibility, robustness, and the ability to address ongoing challenges related to payload capacity, adaptive information embedding and retrieval, and computational efficiency. This study explores the fields of steganography and steganalysis, focusing on embedding and extraction methods from conventional methods (non‐neural network [N‐NN]) to NN architectures. The insights presented serve as a foundational reference for transitioning from conventional embedding and decoding approaches to advanced NN‐driven techniques, thereby enhancing security, efficiency, and resilience in covert communication systems.