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

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%.