The Role of Deep Learning Innovations With CNNs and GANs in Steganography

Abstract

Steganography, the practice of concealing information within other non-secret data, has evolved significantly with the advent of digital technologies. Traditional methods often struggle with maintaining a balance between capacity, imperceptibility, and robustness. Recent advancements in deep learning offer promising enhancements to steganographic techniques, addressing these challenges more effectively. This chapter explores how deep learning approaches can enhance steganography, focusing on the utilization of neural networks for embedding and extracting hidden information. Deep learning models, particularly convolutional neural networks (CNNs) and generative adversarial networks (GANs), have shown superior capability in learning intricate patterns and representations. CNNs can be employed to optimize the feature selection process, ensuring that hidden information is embedded in less noticeable regions of the cover media, thereby improving imperceptibility.