Automatic Detection of CSA Media by Multi-modal Feature Fusion for Law Enforcement Support

Christian Schulze, Dominik Henter, Damian Borth, Andreas Dengel
Proceedings of the ACM International Conference on Multimedia Retrieval 2014, Glasgow, United Kingdom, ACM, New York, NY, USA, 2014

Abstract:

The growing amounts of multimedia data being made available and shared via the Internet pose an increasing problem for law enforcement to investigate the distribution and possession of child sexual abuse (CSA) media. In this paper we address the automatic detection of CSA material in image and video data by multi-modal feature description. Instead of analyzing hash sums or file names, we propose the content-based analysis on visual and, in case of videos, also audio features. To this end, we apply multiple low level features as well as SentiBank, a novel mid-level representation of visual content. In collaboration with police partners and European cyber crime units, we conducted experiments on several datasets, including real world CSA media. Our quantitative evaluation reveals the challenging nature of child pornography detection, especially in the joint presence of non-illegal pornographic data, rendering skin detection, a popular feature for detecting pornography, less discriminative. Further, the utilization of SentiBank features shows high potential for detection and explainability of such content. Overall, multi-modal feature fusion can achieve an improved detection accuracy, reducing equal error rate from 17% to 10% for images and from 16% to 8% for videos as compared to best single feature performance for the chal- lenging task of classifying CSA content from adult media.

BibTex:

@inproceedings{ SCHU2014,
	Title = {Automatic Detection of CSA Media by Multi-modal Feature Fusion for Law Enforcement Support},
	Author = {Christian Schulze and Dominik Henter and Damian Borth and Andreas Dengel},
	BookTitle = {Proceedings of the ACM International Conference on Multimedia Retrieval 2014},
	Year = {2014},
	Publisher = {ACM}
}

     
Last modified:: 30.08.2016