Automatic Detection of Child Pornography using Color Visual Words

Adrian Ulges, Armin Stahl

In: Proceedings of the IEEE International Conference on Multimedia and Expo. IEEE International Conference on Multimedia and Expo (ICME-11) July 11-15 Barcelona Spain IEEE 2011.


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.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence