Traceable AI-driven Avatars Using Multi-factors of Physical World and Metaverse

Metaverse allows users to delegate their AI models to an AI engine, which builds corresponding AI-driven avatars to provide immersive experience for other users. Since current authentication methods mainly focus on human-driven avatars and ignore the traceability of AI-driven avatars, attackers may delegate the AI models of a target user to an AI proxy program to perform impersonation attacks without worrying about being detected.In this paper, we propose an authentication method using multi-factors to guarantee the traceability of AI-driven avatars. Firstly, we construct a user’s identity model combining the manipulator’s iris feature and the AI proxy’s public key to ensure that an AI-driven avatar is associated with its original manipulator. Secondly, we propose a chameleon proxy signature scheme that supports the original manipulator to delegate his/her signing ability to an AI proxy. Finally, we design three authentication protocols for avatars based on the identity model and the chameleon proxy signature to guarantee the virtual-to-physical traceability including both the human-driven and AI-driven avatars.Security analysis shows that the proposed signature scheme is unforgeability and the authentication method is able to defend against false accusation. Extensive evaluations show that the designed authentication protocols complete user login, avatar delegation, mutual authentication, and avatar tracing in about 1s, meeting the actual application needs and helping to mitigate impersonation attacks by AI-driven avatars.

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Publication Details

Published
2024-12-17
DOI
https://doi.org/10.1109/trustcom63139.2024.00326
Citations
1
Primary Topic
Diverse Topics in Contemporary Research
Type
article
Field-Weighted Citation Impact
4.29

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article

Traceable AI-driven Avatars Using Multi-factors of Physical World and Metaverse

Zhenyong Zhang, Youliang Tian, Kedi Yang
1 citations
Diverse Topics in Contemporary Research
4.29
article

Traceable AI-driven Avatars Using Multi-factors of Physical World and Metaverse

Zhenyong Zhang, Youliang Tian, Kedi Yang
article en
1 citations

Abstract

Metaverse allows users to delegate their AI models to an AI engine, which builds corresponding AI-driven avatars to provide immersive experience for other users. Since current authentication methods mainly focus on human-driven avatars and ignore the traceability of AI-driven avatars, attackers may delegate the AI models of a target user to an AI proxy program to perform impersonation attacks without worrying about being detected.In this paper, we propose an authentication method using multi-factors to guarantee the traceability of AI-driven avatars. Firstly, we construct a user’s identity model combining the manipulator’s iris feature and the AI proxy’s public key to ensure that an AI-driven avatar is associated with its original manipulator. Secondly, we propose a chameleon proxy signature scheme that supports the original manipulator to delegate his/her signing ability to an AI proxy. Finally, we design three authentication protocols for avatars based on the identity model and the chameleon proxy signature to guarantee the virtual-to-physical traceability including both the human-driven and AI-driven avatars.Security analysis shows that the proposed signature scheme is unforgeability and the authentication method is able to defend against false accusation. Extensive evaluations show that the designed authentication protocols complete user login, avatar delegation, mutual authentication, and avatar tracing in about 1s, meeting the actual application needs and helping to mitigate impersonation attacks by AI-driven avatars.

Guizhou University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China, Science and Technology Program of Guizhou Province
Openalex Percentile: Top 4%
Diverse Topics in Contemporary Research
4.29
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Traceable AI-driven Avatars Using Multi-factors of Physical World and Metaverse — Zhenyong Zhang, Youliang Tian, et al. · (2024) | TGRS Research Map | TGRS