Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses

TL;DR

This work analyzes security, privacy, and trustworthiness challenges for AI-driven XR metaverse applications, proposing a taxonomy of threats and corresponding defenses across ML layers and XR components. It presents a metaverse-specific case study to illustrate adversarial threats within a realistic AI-XR pipeline and discusses open research directions from adversarial defenses to edge AI and ethical data pipelines. By mapping ML vulnerabilities to the layered metaverse architecture, the paper provides a framework to design secure, private, robust, and trustworthy AI-XR systems. The expected impact is to guide researchers and practitioners in building safer AI-XR metaverse services that respect user privacy, ensure fairness, and maintain user trust. The contributions offer a comprehensive reference for developing secure, trustworthy, and ethically aligned AI-XR metaverse technologies.

Abstract

Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s). The advancements in different technologies like augmented reality, virtual reality, extended reality (XR), artificial intelligence (AI), and 5G/6G communication will be the key enablers behind the realization of AI-XR metaverse applications. While AI itself has many potential applications in the aforementioned technologies (e.g., avatar generation, network optimization, etc.), ensuring the security of AI in critical applications like AI-XR metaverse applications is profoundly crucial to avoid undesirable actions that could undermine users' privacy and safety, consequently putting their lives in danger. To this end, we attempt to analyze the security, privacy, and trustworthiness aspects associated with the use of various AI techniques in AI-XR metaverse applications. Specifically, we discuss numerous such challenges and present a taxonomy of potential solutions that could be leveraged to develop secure, private, robust, and trustworthy AI-XR applications. To highlight the real implications of AI-associated adversarial threats, we designed a metaverse-specific case study and analyzed it through the adversarial lens. Finally, we elaborate upon various open issues that require further research interest from the community.

Figures (11)

• Figure 1: Organization of the paper.
• Figure 2: An overview of different concepts related to metaverse that include VR, AR, MR, XR, virtual metaverse, and augmented metaverse.
• Figure 3: Historical evolution of technologies needed for metaverse
• Figure 4: Applications of XR in metaverse.
• Figure 5: Illustration of different layers in metaverse.