Automatic detection of child pornography using color visual words

This paper addresses the computer-aided detection of child sexual abuse (CSA) images, a challenge of growing importance in multimedia forensics and security. In contrast to previous solutions based on hashsums, file names, or the retrieval of visually similar images, we introduce a system which employs visual recognition techniques to automatically identify suspect material. Our approach is based on color-enhanced visual word features and a statistical classification using SVMs. The detector is adapted to CSA material in a training step. In collaboration with police partners, we have conducted a quantitative evaluation on several datasets (including real-world CSA material). Our results indicate that recognizing child pornography is a challenging problem (more difficult than the detection of regular porn). Yet, while skin detection - a popular approach in pornography detection - fails, our approach can achieve a prioritization of content (equal error 11 – 24%) to improve the efficiency of forensic investigations of child sexual abuse. Examples illustrate that the system employs color cues as key features for discriminating CSA content.