Video Steganography Based on Invertible Neural Networks With DenseNet and U-Net Features

Abstract

The increased rate of cyber threats such as fraud and other forms of attack, including phishing, malware, and denial-of-service attacks, has led to a growing demand for secure methods to ensure the security of sensitive information that is transferred among users. Video steganography has become a very important element in dealing with these security issues, as communication has become more dependent on multimedia content. Hiding information is now an area that is being developed very fast with the introduction of Deep Learning (DL)-based steganography methods. This framework introduces Vid_Steg_DenseUNet, a video steganography method which efficiently extracts multi-level features using edge-preserving U-Net and DenseNet through Invertible Neural Networks (INN) interactions. The main objectives of this approach include improving the perceptual fidelity of the stego video and enhancing the quality of the recovered secret image. These improvements are evaluated using objective imperceptibility metrics through pixel-level and structural analyses, such as PSNR and SSIM. The model was trained on the DIV2K images and Ultra Video Group (UVG) videos. The proposed model hides a secret color image in each video frame, and its performance is assessed using quantitative difference metrics. Comparative results with existing state-of-the-art methods, such as recent DL-based video steganography frameworks, show that this end-to-end embedding approach delivers promising performance in terms of human visual accuracy, with a PSNR score of 37.054 and an SSIM score of 0.9754. The results also demonstrate that the model achieves enhanced security and high resistance to detection techniques.