Filtering adult image content with topic models
Protecting children from exposure to adult content has become a serious problem in the real world. Current statistics show that, for instance, the average age of first Internet exposure to pornography is 11 years, that the largest consumer group of Internet pornography is the age group of 12-to-17- year-olds and that 90% of the 8-to-16-year-olds have viewed porn online. To protect our children, effective algorithms for detecting adult images are needed. In this research we evaluate the use of probabilistic Latent Semantic Analysis (pLSA) for this task. We will show that topic models based on pLSA can detect adult content with a correct positive rate of 92.7%, while only showing off a false positive rate of 1.9%. Even when using grayscale images only, a correct positive rate of 90.8% at a false positive rate of 2% can be achieved.