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With the increasing amount of pornography being uploaded on the Internet today, arises the need to detect and block such pornographic websites, especially in Eastern cultural countries. Studying pornographic images and videos, show that explicit sensitive objects are typically one of the main characteristics portraying the unique aspect of pornography content. This paper proposed a classification method on pornographic visual content, which involved detecting sensitive objects using object detection algorithms. Initially, an object detection model is used to identify sensitive objects on visual content. The detection results are then used as high-level features combined with two other high-level features including skin body and human presence information. These high-level features finally are fed into a fusion Support Vector Machine (SVM) model, thus draw the eventual decision. Based on 800 videos from the NDPI-800 dataset and the 50.000 manually collected images, the evaluation results show that our proposed approach achieved 94.06% and 94.88% in Accuracy respectively, which can be compared with the cutting-edge pornographic classification methods. In addition, a pornographic alerting and blocking extension is developed for Google Chrome to prove the proposed architecture's effectiveness and capability. Working with 200 websites, the extension achieved an outstanding result, which is 99.50% Accuracy in classification.

This paper addresses video inter-intra similarity retrieval for pornographic classification. The main approaching method is obtaining the internal representation and external similarity between a single unlabeled video and batches of labeled videos, then combining together to determine its label. For the internal representation, we extracted inner features within frames and clustered them to find the representative centroid as the intra-feature. For the external similarity, we utilized a similarity video learning named ViSiL to calculate distance score between two videos using chamfer similarity. With distance scores between input video and batches of pornographic/nonpornographic videos, the inter feature of the input video is obtained. Finally, the inter similarity vector and the intra representation are then concatenated together and fed to a final classifier to identify whether the video is for adults or not. In experiment, our method performs 96.88% accuracy on NPDI-2k, achieved a comparative result comparing to other state-of-the-art methods on the pornographic classification problem.

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