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
involved.
Applying deep learning to classify pornographic images and videos

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