Steganography

Artificial intelligence

Payload (computing)

Image (mathematics)

Network packet

Data mining

Computer science

Steganalysis

Computer security

IEEE Transactions on Industrial Informatics

The rapid development of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) brings new security threats by exposing secret and private data. Thus, information security has become a major concern in the communication environment of IIoT and AI, where security and privacy must be ensured for the messages between a sender and the intended recipient. In this article, we propose a method called Harris hawks optimization-integer wavelet transform (HHO-IWT) for covert communication and secure data in the IIoT environment based on digital image steganography. The method embeds secret data in the cover images using a metaheuristic optimization algorithm called HHO to efficiently select image pixels that can be used to hide bits of secret data within integer wavelet transforms. The HHO-based pixel selection operation uses an objective function evaluation depending on the following two phases: exploitation and exploration. The objective function is employed to determine an optimal encoding vector to transform secret data into an encoded form generated by the HHO algorithm. Several experiments are conducted to validate the performance of the proposed method with respect to visual quality, payload capacity, and security against attacks. The obtained results reveal that the HHO-IWT method achieves higher levels of security than the state-of-the-art methods and that it resists various forms of steganalysis. Thus, utilizing this approach can keep unauthorized individuals away from the transmitted information and solve some security challenges in the IIoT.

A Novel Image Steganography Method for Industrial Internet of Things Security