Applying deep learning to classify pornographic images and videos

Applying deep learning to classify pornographic images and videos

_Abstract. It is no secret that pornographic material is now a one-_clickaway

from everyone, including children and minors. General social media

networks are striving to isolate adult images and videos from normal

ones. Intelligent image analysis methods can help to automatically

detect and isolate questionable images in media. Unfortunately, these

methods require vast experience to design the classifier including one or

more of the popular computer vision feature descriptors. We propose to

build a classifier based on one of the recently flourishing deep learning

techniques. Convolutional neural networks contain many layers for both

automatic features extraction and classification. The benefit is an easier

system to build (no need for hand-crafting features and classifiers). Additionally,

our experiments show that it is even more accurate than the

state of the art methods on the most recent benchmark dataset.

Conclusions: We proposed applying convolutional neural networks to automatically classify

pornographic images and videos. We showed that our proposed fully automated

solution outperformed the accuracy of hand-crafted feature descriptors solutions.

We are continuing our research to find an even better network architecture for

this problem. Nevertheless, all the successful applications so far rely on supervised

training methods. We expect a new wave of deep learning networks would

emerge by combining supervised and unsupervised methods where a network

can learn from its mistakes while in actual deployment. We believe further research

can also be directed toward allowing machines to consider the context

and overall rhetorical meaning of a video clip while relating them to the images