A hybrid chaotic map with coefficient improved whale optimization-based parameter tuning for enhanced image encryption

The security of the data becomes the main concern due to the quick rise in the exchange of data over the open networks and the Internet. The cryptographic techniques based on chaos theory reveal some novel and effectual orders to develop secure image encryption approaches. Images are the most attractive kinds of data in the encryption domain. In current years, the chaos-based cryptographic techniques have provided an efficient way to expand secure image encryption frameworks. This proposal temps to develop an optimized HCM for promoting novel image encryption. The proposed image encryption model involves 4 steps, such as image pre-processing, key generation, image encryption using optimized HCM, and image decryption. In the pre-processing, the RGB image is converted into a grayscale image, and the key is generated by the SHA-256 cryptographic hash algorithm. Further, the encryption of the image is performed by HCM with the integration of 2DLCM, and PWLCM. While hybridizing the two chaotic maps for image encryption, parameter tuning or optimization is performed for improving its performance. Moreover, Information entropy is considered as the objective model that has to be maximized while tuning the parameters, and the maximum value of information entropy will mean the best performance. An improvement in the well-known optimization algorithms termed as CI-WOA is adopted for performing the parameter optimization of HCM. Hence, the optimized HCM-based image encryption can be confirmed as an efficient and secure way for all types of image transmissions.