Secure and trustworthy artificial intelligence-extended reality (AI-XR) for metaverses

Qayyum, A., Butt, M. A., Ali, H., Usman, M., Halabi, O., Al-Fuqaha, A., Abbasi, Q. , Imran, M. A. and Qadir, J. (2023) Secure and trustworthy artificial intelligence-extended reality (AI-XR) for metaverses. ACM Computing Surveys, (doi: 10.1145/3614426) (In Press)

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.

• Item Type: Articles
• Additional Information: The authors would like to acknowledge support from Qatar University High Impact Internal Grant QUHI-CENG23/24-127.
• Status: In Press
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Qayyum, Adnan and Abbasi, Professor Qammer
• Authors: Qayyum, A., Butt, M. A., Ali, H., Usman, M., Halabi, O., Al-Fuqaha, A., Abbasi, Q., Imran, M. A., and Qadir, J.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: ACM Computing Surveys
• Publisher: ACM Press
• ISSN: 0360-0300
• ISSN (Online): 1557-7341
• Published Online: 10 August 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in ACM Computing Surveys 2023
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher