Paper Info
Title
Automatic detection of child pornography using color visual words
Abstract
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.
Year
DOI
Venue
2011
10.1109/ICME.2011.6011977
ICME
Keywords
Field
DocType
child pornography,automatic detection,popular approach,pornography detection,csa content,real-world csa material,csa material,color visual word,skin detection,computer-aided detection,suspect material,child sexual abuse,feature extraction,visualization,support vector machines,skin,materials
Computer vision,Child pornography,Visualization,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Statistical classification,Pornography,Content-based image retrieval,Visual Word
Conference
ISSN
Citations 
PageRank 
1945-7871
21
0.80
References 
Authors
15
2
Name
Order
Citations
PageRank
Adrian Ulges132826.61
Armin Stahl233926.74