Exploring Advanced Deep Learning Methods for Image Steganography: New Approaches to Concealing Videos Within Videos

Steganography is the art of concealing information within another medium, traditionally involving the embedding of text within images. However, an increasingly relevant application is the concealment of one image within another, known as image-in-image steganography. Extending this concept further, this research introduces the innovative approach of video-in-video steganography, which applies image-in-image techniques to conceal entire video sequences within other videos. By leveraging the capabilities of Deep Learning, specifically convolutional neural networks, improving the undetectability and efficiency of our steganographic methods. Focus on refining how visual data is concealed within other multimedia content leads to more structured and cohesive embedding strategies. The models were trained using instance the popular ImageNet dataset [1], showcasing proficiency in embedding secret images and video sequences within host media, undetectably. This work evaluated the performance of our models using Structural Similarity Index Measure (SSIM) to assess their capacity to encode and decode secret data effectively. The primary contribution of our study is the development of a system capable of performing steganography between two videos of the same length, thereby offering a novel perspective within the field of traditional steganography. This exploration not only pushes the boundaries of conventional techniques but also paves new pathways for secure visual data transmission, highlighting the transformative potential of convolutional neural networks in revolutionizing steganography.