A survey on securing image-centric edge intelligence

Abstract

Facing enormous data generated at the network edge, edge intelligence (EI) emerges as the fusion of edge computing and AI, revolutionizing edge data processing and intelligent decision-making. Nonetheless, this emergent mode presents a complex array of security challenges, particularly prominent in image-centric applications due to the sheer volume of visual data and its direct connection to user privacy. These challenges include safeguarding model/image privacy and ensuring model integrity against various security threats, such as model poisoning. Essentially, those threats originate from data attacks, suggesting data protection as a promising solution. Although data protection measures are well-established in other domains, image-centric EI necessitates focused research. This survey examines the security issues inherent to image-centric EI and outlines the protection efforts, providing a comprehensive overview of the landscape. We begin by introducing EI, detailing its operational mechanics and associated security issues. We then explore the technologies facilitating security enhancement (e.g., differential privacy) and EI (e.g., compact networks and distributed learning frameworks). Next, we categorize security strategies by their application in data preparation, training, and inference, with a focus on image-based contexts. Despite these efforts on security, our investigation identifies research gaps. We also outline promising research directions to bridge these gaps, bolstering security frameworks in image-centric EI applications.