A High Order Video Compressive Sensing Encryption Using Fractional Order Hyper Chaotic System with Intelligent Scrambling and Nucleotide Sequences

Abstract

In trending research arena, image encryption is taken as one of the important security aspect and has drawn the attention of many researchers around the globe. There are many literatures available where different methods are proposed for improving the encryption standards for a secured image. In the recent times, the chaotic techniques are made used for the improvement in encryption standards of the image. These are also applied many domains such as in design of cryptosystems and also in encryption of image. The chaotic techniques based encryptions are novel encryption techniques. Random chaos based sequences are made used for the process of encryption and has also proven fast. One of the major setback in using this technique is the less accuracy. A video compressive encryption method is proposed to accomplish compression as well as encryption based technique that incorporate compressive sensing (CS), pixel level scrambling, bit level scrambling and Nucleotide Sequences operations. The proposed method employs fractional order hyper-chaotic chen system for generation of measurement matrix. The matrix measurements are done through the hyper chaotic with intelligent scrambling in pixel level thus strengthens the efficiency of the encryption process. The CS measurement is scrambled to its pixel level to resist plain text attack, bit level scrambling reduces the correlation between the adjacent measurements and the operations of the nucleotide sequence are performed on the scrambled bits, enhancing the encryption. The reconstruction technique includes two stages, the first stage of which utilizes intra-frame similarity and offers strong preliminary retrieval for each frame, and the stage two iteratively improves the efficiency of reconstruction by integrating interframe multi hypothesis (MH) estimation and weighted residual sparsity modeling. By using a mathematical approach based on the MH predictions, the weights of the residual coefficients are modified in each iteration and Split Bregman iteration algorithm is devise to resolve weighted l1 regularization. The experimental result are compared between proposed and existing approaches like HC-DNA, C-DNA, CDCP and CHC. Experimental findings show that the proposed algorithm provides good compression of video coupled with an efficient encryption method that is resistant to multiple attacks.