Review of Video Steganography by using Deep Learning Methods: Datasets, Techniques, and Evaluations

The growing prevalence of cyber threats, including fraud and attacks, has intensified the demand for secure methods of safeguarding confidential information exchanged between users. As telecommunications increasingly rely on multimedia data, video steganography has become a prominent technique to address these concerns. By embedding sensitive data within video files, this approach enhances protection against unauthorized access and common internet-based attacks, offering a robust layer of security in an era of escalating digital risks. With the introduction of Deep Learning (DL) steganography methods recently, video steganography can be defined as a rapidly developing subject within information security. This study provides a thorough analysis of the many DL-based video steganography methods that have been covered in the literature. Those methods usually have two modules: a decoder and an encoder. Those techniques have assisted in resolving issues with visual quality as well as capacity, which have been previously evident in older techniques. The study also presented a description of the main databases used in DL-based video steganography, where the University of Central Florida 101 (UCF101) database is the most used in this field. Mean Squared Error (MSE), Peak-Signal Noise-Ratio (PSNR), Average Pixel Difference (APD), and Structural Similarity Index (SSIM) have been the top-rated evaluation metrics utilized to assess the efficacy of the video steganography method. All things considered, this paper bridged the gap between two research domains, video steganography and DL, and was a useful tool for academics studying the subject. Previous studies have demonstrated that Reversible Neural Networks (RNNs) and Generative Adversarial Networks (GANs), which rely on Convolutional Neural Networks (CNNs), have given high results.