Bioinformatics-Inspired Secure Video Encryption Using Chaotic Maps and Light Streaming Algorithms

The Traditional encryption methods, such as AES, can be too slow for large video files, necessitating the development of faster and more efficient solutions for real-time video security. This research aims to enhance video security during transmission by developing a novel video encryption algorithm that is both fast and robust, specifically designed to handle large video datasets efficiently. The proposed video encryption algorithm integrates lightweight streaming algorithms for enhanced speed, modern cryptographic techniques, and chaotic maps for heightened security. The encryption process involves three key steps: Dividing the video into frames and scrambling/encrypting each frame, employing three innovative techniques: vertical image cutting to create two halves, utilizing the Lorenz Chaotic System map in the scrambling and shifting steps, applying a bioinformatics technique (tRNA) for encrypting red and green color channels, and applying an XOR operation between the output of step 2 and a key generated via a chaotic map. Unlike traditional methods, the proposed approach processes all frames without requiring separation procedures. Evaluation based on metrics such as PSNR, MSE, NPCR, SC, similarity, UACI, histogram, EQ, and entropy demonstrates the algorithm's robustness. Experimental results reveal that the proposed algorithm achieves faster encryption times and superior encryption effectiveness compared to AES. The system's complexity is bolstered by requiring extensive chaos parameters, the Lorenz Chaotic System map, visual cryptography techniques, and advanced processes for generating S-boxes and keys. High-quality security metrics validate the algorithm's robustness and effectiveness. The proposed video encryption offers a faster and secure solution for encrypting large video files.