Machine Learning-Fused Hybrid Image Encryption Framework Using Hardware-Optimized AES Algorithm

Image encryption protects sensitive information against malicious attacks in various applications, including the Internet of Things (IoT). However, lower computational complexity compromises the security in image encryption. Computationally intensive encryption algorithms make the system unreliable for real-time scenarios. This research work fuses machine learning and encryption algorithm. In the proposed work, blocks are constructed from an image, and for each block, features such as entropy, energy, contrast, and homogeneity are utilized to train the classifier to classify the block as sparse, intermediate, or dense. Sparse block containing less information and is encrypted with Gray codes, whereas, the intermediate and dense blocks holding more information are encrypted using advanced encryption standard (AES) to reduce processing time. In addition, the hardware-optimized proposed hybrid image encryption framework further decreases the computational time required for image encryption. The proposed AES hardware implementation enhances both throughput and area efficiency through composite field arithmetic (CFA) in GF((2^4)^2) , facilitating a combined encryption and decryption architecture. Optimized arithmetic operations reduce hardware utilization. The pipelined key expansion and SubByte architecture enhance the computation time. The proposed design is implemented on Virtex-6 XC6VLX240T for AES-128 and utilizes less hardware than existing techniques while achieving 5.14 Gbps throughput. The proposed hybrid image encryption framework encrypts a 512× 512 grayscale image with rich in information within 22.35ms with optimal security.