A systematic review of innovations for real-time image security in IoT applications using machine learning and blockchain

Image security in the context of Internet of Things (IoT) has gained significant attention due to the increasing demand for timely and effective protection against unauthorized access and tampering. This paper presents a systematic literature review (SLR) that comprehensively examines the advancements in machine learning (ML) techniques for real-time image security in IoT, with a specific attention on the integration of blockchain technology. The SLR follows a rigorous methodology, including search strategy, study selection, data analysis, and quality assessment, to identify and evaluate relevant research papers. The findings of the SLR reveal the potential of ML techniques, coupled with blockchain, in enhancing image security in IoT. The reviewed papers demonstrate advancements in data perturbation, data leakage and privacy concerns, IoT data vulnerability, medical data encryption, network security, cyber manufacturing system risks, diagnosis precision, privacy issues in centralized architectures, and various image security attacks. The paper also discusses the limitations and future directions of research in this field, such as the need for addressing scalability, integration with existing systems, and regulatory considerations. The implications for research and practice emphasize the importance of bridging the gap between theoretical advancements and practical implementations, as well as the ethical and legal implications associated with image security in IoT.