A Peek into the Metaverse: Detecting 3D Model Clones in Mobile Games


Chaoshun Zuo, Chao Wang, and Zhiqiang Lin, The Ohio State University


3D models are indispensable assets in metaverse in general and mobile games in particular. Yet, these 3D models can be readily extracted, duplicated, or cloned, a reality that poses a considerable threat. Although instances of games duplicating 3D models from other games have been documented, the pervasiveness of this issue remains unexplored. In this paper, we undertake the first systematic investigation of 3D model cloning within mobile games. However, multiple challenges have to be addressed for clone detection, including scalability, precision, and robustness. Our solution is 3DSCAN, an open source 3D Scanning tool for Clone Assessment and Notification. We have evaluated 3DSCAN with about 12.2 million static 3D models and 2.5 million animated 3D models extracted from 176K mobile games. With these 3D models, 3DSCAN determined that 63.03% of the static models are likely cloned ones (derived from 1,046,632 distinct models), and 37.07% animated 3D models are likely cloned ones (derived from 180,174 distinctive models). With a heuristic-based clone detection algorithm, 3DSCAN finally detected 5,238 mobile games likely containing unauthorized 3D model clones.

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@inproceedings {291192,
author = {Chaoshun Zuo and Chao Wang and Zhiqiang Lin},
title = {A Peek into the Metaverse: Detecting 3D Model Clones in Mobile Games},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {3925--3942},
url = {https://www.usenix.org/conference/usenixsecurity23/presentation/zuo},
publisher = {USENIX Association},
month = aug

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