Pornography detection in video benefits (a lot) from a multi-modal approach
Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis
An one-class classification approach to detecting porn image
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Towards detection of child sexual abuse media: categorization of the associated filenames
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Classification and Recovery of Fragmented Multimedia Files using the File Carving Approach
International Journal of Mobile Computing and Multimedia Communications
A survey on visual adult image recognition
Multimedia Tools and Applications
Optical Memory and Neural Networks
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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.