Applying deep learning to classify pornographic images and videos

TL;DR

This paper presents a deep learning-based classifier using convolutional neural networks to automatically detect pornographic images and videos, aiming to improve accuracy and ease of system development over traditional methods.

Contribution

The paper introduces a CNN-based approach for classifying adult media, demonstrating superior accuracy compared to existing state-of-the-art techniques on recent benchmarks.

Findings

CNN classifier outperforms traditional methods

Automatic feature extraction simplifies system design

Achieves higher accuracy on benchmark datasets

Abstract

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.