Deep Convolutional Q-Learning system and VGG-16 steganalysis detector for image Steganography

Image steganography is the concealment of data (text, image, or video) within a cover image. The confidential information is concealed so that human eyes cannot see it. In recent years, deep learning has garnered popularity as a potent tool for various applications, including image steganography. Image steganography techniques are increasingly used for covert communication and information concealment because of the accelerated development of digital technology. This paper uses Deep Convolutional Generative Adversarial Networks (DCGANs) to incorporate classified data within cover images. Our proposed steganography method employs DCGANs to generate realistic-looking stego images invisible to the human eye while effectively concealing sensitive information. Moreover, we present a novel Deep Convolutional Q-Learning (DCQL) method for optimizing the embedding procedure, ensuring the utmost imperceptibility while preserving data integrity. We use a cutting-edge VGG-16-based steganalysis detector that employs deep learning algorithms to scrutinize suspicious images for hidden content to uncover these hidden signals. The VGG-16 architecture, with its established image classification abilities, is ideally adapted for the challenging task of steganalysis, which involves locating even the most subtly concealed information. Our experimental results demonstrate that the proposed method effectively conceals and detects steganography content. We evaluate the system's efficacy using various steganography techniques and cover image datasets, highlighting its robustness and adaptability. In addition, the DCQL procedure increases the invisibility of stego-images while preserving the integrity of the hidden data. The combination of DCGANs, DCQL, and VGG-16-based steganalysis detectors results in a comprehensive framework that improves the security and detection of image steganography, with promising applications in cyber security and digital forensics.