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.



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