A dual-layer clustering scheme for real-time identification of plagiarized massive multiplayer games (MMG) assets

Raffe, W, Hu, J, Zambetta, F and Xi, K 2010, 'A dual-layer clustering scheme for real-time identification of plagiarized massive multiplayer games (MMG) assets', in Jing Bing Zhang, Chun-Liang Lin, and Bor-Ren Lin (ed.) Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, Taichung, Taiwan, 15-17 June 2010, pp. 307-312.


Document type: Conference Paper
Collection: Conference Papers

Title A dual-layer clustering scheme for real-time identification of plagiarized massive multiplayer games (MMG) assets
Author(s) Raffe, W
Hu, J
Zambetta, F
Xi, K
Year 2010
Conference name The 5th IEEE Conference on Industrial Electronics and Applications
Conference location Taichung, Taiwan
Conference dates 15-17 June 2010
Proceedings title Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications
Editor(s) Jing Bing Zhang, Chun-Liang Lin, and Bor-Ren Lin
Publisher IEEE
Place of publication NJ, USA
Start page 307
End page 312
Total pages 6
Abstract Theft of virtual assets in massive multiplayer games (MMG) is a significant issue. Conventional image based pattern and object recognition techniques are becoming more effective identifying copied objects but few results are available for effectively identifying plagiarized objects that might have been modified from the original objects especially in the real-time environment where a large sample of objects are present. In this paper we present a dual-layer clustering algorithm for efficient identification of plagiarized MMG objects in an environment with real-time conditions, modified objects and large samples of objects are present. The proposed scheme utilizes a concept of effective pixel banding for the first pass clustering and then uses Hausdorff Distance mechanism for further clustering. The experimental results demonstrate that our method drastically reduces execution time while achieving good performance of identification rate, with a genuine acceptance rate of 88%.
Subjects Networking and Communications
Computer System Security
Information Systems Management
Keyword(s) component
patter recognition
clustering
MMG security
DOI - identifier 10.1109/ICIEA.2010.5516844
Copyright notice Copyright © 2010 IEEE.
ISBN 9781424450466
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