Abstract: The Internet has become an essential part of everyday life. It links people with enormous amounts of information covering almost any topic imaginable. However harmful or inappropriate information such as pornography can also be easily found on the web which should not always be available, especially to minors. Internet filters are typically used to block such inappropriate content. These are largely based on the metadata related to the websites or by directly blocking the URLs related to those websites. How-ever seemingly innocuous websites can contain undesirable images that should not be accessible to children. In this paper, we describe how images and videos can automatically be identified (classified) without any human supervision based on their subject matter. To achieve this, we apply deep learning methods to detect and classify adult-only image content from both images and live videos. We use the TensorFlow library and two pre-trained models: MobileNet_v1 and Inception_v3, with an official (academic) pornography dataset including associated labelling. The performance of each model was investigated. The final solution was delivered as an iOS application to detect and classify photos and live videos based on their adult-only content. The app achieved an accuracy of over 92%.
Authors: Richard Sinnott (University of Melbourne, Australia)