Lightweight Image Crypto-Compression Using Haar Transform and Selective Encryption for Grayscale IoT Images

With the advent of the Multimedia Internet of Things (MIoT), many image compression techniques have been proposed to address the network’s considerable challenges related to performance and security. However, many MIoT devices, such as the nRF52832 SoC with 64Kb RAM or even less, have significant memory constraints, making conventional methods unsuitable. MIoT networks face considerable challenges related to performance and security due to limitations in the power, computation, and memory of MIoT devices. These limitations result in difficulties in handling high image volumes. Multimedia compression is a potential solution to reduce data size. As MIoT devices often rely on wireless connections, they are also vulnerable to diverse security attacks (passive and active). This work introduces a secure and efficient image crypto-compression technique dedicated to devices having limited memory. It also proposes using denoising and a super-resolution deep learning model to reduce the overhead of the compression process and a lightweight cipher scheme that requires a single round of simple operations to reduce the overhead of the encryption process. The proposed approach effectively addresses the mentioned challenges with minimal overhead on the MIoT device, especially in terms of computational and communication delays, and extensive experimentation underscores its suitability in both effectiveness and robustness.