Spatiotemporal CNNs for Pornography Detection in Videos
This addresses the issue of inappropriate content exposure for teens and children on the internet, but it is incremental as it builds on existing CNN methods for a specific domain.
The paper tackled the problem of detecting pornography in videos by assessing two spatiotemporal CNNs, VGG-C3D and ResNet R(2+1)D, achieving an accuracy of 95.1% on the Pornography-800 dataset, which outperformed some state-of-the-art methods and was competitive with other CNN-based approaches.
With the increasing use of social networks and mobile devices, the number of videos posted on the Internet is growing exponentially. Among the inappropriate contents published on the Internet, pornography is one of the most worrying as it can be accessed by teens and children. Two spatiotemporal CNNs, VGG-C3D CNN and ResNet R(2+1)D CNN, were assessed for pornography detection in videos in the present study. Experimental results using the Pornography-800 dataset showed that these spatiotemporal CNNs performed better than some state-of-the-art methods based on bag of visual words and are competitive with other CNN-based approaches, reaching accuracy of 95.1%.