Applying deep learning to classify pornographic images and videos
This addresses the challenge of isolating adult content on social media to protect children and minors, representing an incremental improvement in automated detection.
The paper tackles the problem of automatically classifying pornographic images and videos by proposing a deep learning-based classifier using convolutional neural networks, which eliminates the need for hand-crafted features and achieves higher accuracy than state-of-the-art methods on a recent benchmark dataset.
It is no secret that pornographic material is now a one-click-away 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.