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On the Fly Porn Video Blocking Using Distributed Multi-GPU and Data Mining Approach

Urvesh Devani, Valmik B. Nikam, B.B. Meshram
Department of Computer Engineering and Information Technology, Veermata Jijabai Technological Institute, Mumbai, India
International Journal of Distributed and Parallel Systems (IJDPS) Vol.5, No.4, 2014

@article{devani2014fly,

   title={On the fly Porn Video Blocking using Distributed Multi-GPU and Data Mining Approach},

   author={Devani, Urvesh and Nikam, Valmik B and Meshram, BB},

   journal={International Journal of Distributed and Parallel Systems},

   year={2014}

}

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Preventing users from accessing adult videos and at the same time allowing them to access good educational videos and other materials through campus wide network is a big challenge for organizations. Major existing web filtering systems are textual content or link analysis based. As a result, potential users cannot access qualitative and informative video content which is available online. Adult content detection in video based on motion features or skin detection requires significant computing power and time. Judgment to identify pornography videos is taken based on processing of every chunk from video, consisting specific number of frames, sequentially one after another. This solution is not feasible in real time when user has started watching the video and decision about blocking needs to be taken within few seconds. In this paper, we propose a model where user is allowed to start watching any video; at the backend porn detection process using extracted video and image features shall run on distributed nodes with multiple GPUs (Graphics Processing Units). The video is processed on parallel and distributed platform in shortest time and decision about filtering the video is taken in real time. Track record of blocked content and websites is cached, too. For every new video downloads, cache is verified to prevent repetitive content analysis. On the fly blocking is feasible due to latest GPU architecture, CUDA (Compute Unified Device Architecture) and CUDA aware MPI (Message Passing Interface). It is possible to achieve coarse grained as well as fine grained parallelism. Video Chunks are processed parallel on distributed nodes. Porn detection algorithm on frames of chunks of videos can also achieve parallelism using GPUs on single node. It ultimately results into blocking porn video on the fly and allowing educational and informative videos.
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