Detecting pornographic video content by combining image features with motion information
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Recognition of adult images, videos, and web page bags
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Pornography detection in video benefits (a lot) from a multi-modal approach
Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis
Fusing audio vocabulary with visual features for pornographic video detection
Future Generation Computer Systems
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This paper proposes a fast method for detection of indecent video content using repetitive motion analysis. Unlike skin detection, motion will provide invariant features irrespective of race and color. The video material to be evaluated is divided into short fixed-length sections. By filtering different combinations of B-frame motion vectors using adjacency in time and space, one dominant motion vector is constructed for each frame. The power spectral density estimate of this dominant motion vector is then computed using a periodogram with a Hamming window. The resulting power spectrum is then subjected to a Slepian selection window to restrict the spectrum to a limited frequency range typical of indecent movement, as empirically derived by us. A threshold detector is then applied to detect repetitive motion in video sections. However, there are instances where repetitive motion occurs in these shorter sections without the video as a whole being indecent. As a second step, an additional detector can be employed to determine if the sections over a longer period of time can be classified as containing indecent material. The proposed method is resource efficient and do not require the typical IDCT step of video decoding. Further, the computationally expensive spectral estimation calculations are done using only one value per frame. Evaluations performed using a restricted set of videos show promising results with high true positive probability (≫85%) for a low false positive probability (≪10%) for the repetitive motion detection.