A Benchmark Methodology for Child Pornography Detection

• Published in: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) pp. 455 - 462
• Main Authors: Macedo, Joao, Costa, Filipe, A. dos Santos, Jefersson
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: benchmark dataset; child pornography detection; computer forensics; deep learning; Detectors; Estimation; Face; Feature extraction; Image color analysis; Law enforcement; Visualization
• ISSN: 2377-5416
• DOI: 10.1109/SIBGRAPI.2018.00065

The acquisition and distribution of child sexual content are some of the most important concerns for legislative systems and law enforcement agencies around the world. There is a great demand for automatic detection of child pornography, mainly due to the large amount of existent data and the facility someone can share this content over the internet. Although there are some proposed methods to automatically detect child pornography content in the literature, there is no available dataset to assess and compare the performance of these methods due to legal restrictions, considering that in many countries the distribution or possession of this material is a crime by Law. To mitigate this problem, we work with the Brazilian Federal Police to structure and organize a benchmark methodology for child pornography to make it possible the comparison of distinct categories of child pornography detectors. Therefore, we present in this paper the used methodology for the creation of a new annotated dataset of images of child pornography. We also propose a child pornography detection step-wise methodology based on automatic age estimation combined with a pornography detector, which is evaluated using the described benchmark dataset. The proposed approach achieved results (79.84% accuracy) that overcome two tools currently used by the Brazilian Federal Police.