Spotlight on Video Piracy Websites: Familial Analysis Based on Multidimensional Features.

With the gradual increase in awareness of intellectual property protection in recent years, it has become imperative to strengthen the monitoring and regulation of digital piracy. The previous counter-measures suffer from low accuracy or passive data collection. Furthermore, the commonly adopted website clustering methods focus exclusively on a few attributes. The results obtained do not draw a comprehensive picture of the connections between websites within a family. In this paper, we aim to address the issue of digital piracy being challenging to identify, trace, monitor, and regulate in the current situation, utilizing video piracy websites targeting Chinese consumers as an example. The present architecture enables proactive discovery and detection of suspicious websites with a 96.2% accuracy, compensating for traditional digital piracy detection inadequacies. The proposed novel feature extraction method for clustering video piracy websites can synthesize multiple aspects in terms of layout, content, and infrastructure. The clustering results indicate that the websites belonging to the same family obtained by the proposed method show a more comprehensive similarity.