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

Author:

Qayyum Adnan1,Butt Muhammad Atif2,Ali Hassan2,Usman Muhammad3,Halabi Osama4,Al-Fuqaha Ala5,Abbasi Qammer H.6,Imran Muhammad Ali6,Qadir Junaid4

Affiliation:

1. University of Glasgow, United Kingdom and Information Technology University, Pakistan

2. Information Technology University, Pakistan

3. Glasgow Caledonian University, United Kingdom

4. Qatar University, Qatar

5. Hamad Bin Khalifa University, Qatar

6. University of Glasgow, United Kingdom

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.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference177 articles.

1. Martin Abadi , Andy Chu , Ian Goodfellow , H Brendan McMahan , Ilya Mironov , Kunal Talwar , and Li Zhang . 2016 . Deep learning with differential privacy . In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 308–318 . Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 308–318.

2. Amina Adadi and Mohammed Berrada . 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI) . IEEE access 6( 2018 ), 52138–52160. Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access 6(2018), 52138–52160.

3. James Aiken and Sandra Scott-Hayward . 2019 . Investigating adversarial attacks against network intrusion detection systems in sdns . In 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 1–7. James Aiken and Sandra Scott-Hayward. 2019. Investigating adversarial attacks against network intrusion detection systems in sdns. In 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 1–7.

4. Advances in adversarial attacks and defenses in computer vision: A survey;Akhtar Naveed;IEEE Access,2021

5. Asad Ali , Inaam Ilahi , Adnan Qayyum , Ihab Mohammed , Ala Al-Fuqaha , and Junaid Qadir . 2021. Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic Review. TechRxiv ( 2021 ). Asad Ali, Inaam Ilahi, Adnan Qayyum, Ihab Mohammed, Ala Al-Fuqaha, and Junaid Qadir. 2021. Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic Review. TechRxiv (2021).

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Consensual XR: A Consent-Based Design Framework for Mitigating Harassment and Harm Against Marginalized Users in Social VR and AR;2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct);2023-10-16

2. Security Risks, User Privacy Risks, and a Trust Framework for the Metaverse Space;2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom);2023-06

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