A Secure and Efficient Optimized Image Encryption Using Block Compressive Sensing and Logistic Map Method

Recently, multimedia has developed and become very important for transferring images securely through public networks. This paper uses the COOT optimization algorithm with compressive sensing (CS) for image encryption. A good method was proposed for encryption using compressive sensing with COOT optimization and chaos to encrypt images and obtain optimal encryption with the least correlation between pixels. This method will strengthen the encryption against various types of attacks. The natural image was sparsed using discreet wavelet transform (DWT) and the FAN transform. The image is divided into several blocks, and CS is applied to each block. The best measurement matrix was obtained using a COOT-optimized algorithm. All blocks are masked to get the compressed image, and the pixels are quantified. Next, the COOT optimization is used to Shuffle the image pixels to achieve the minimum correlation between the pixels. Then, a logistic map will be used to uniform the image pixel values by diffusion to get the final encrypted image. Chen’s chaotic and logistic map initial values are obtained from the input image after its division into four parts by taking a value from each part. The evaluation results obtained for this algorithm showed that it performs highly compared to other conventional methods. The average PSNR for the reconstructed images was 35.244, the average NPCR and UACI were 90.53 and 29.54, respectively, and the average correlation was (D = 0.0018, V = 0.0031, H = 0.0039). The results proved that the method is strong enough and very efficient to withstand attacks. © 2024 River Publishers.