This paper introduces and evaluates a new framework for identifying Sexually Exploitative Imagery of Children through the use of automation.
This paper introduces a novel framework for automatically identifying Sexually Exploitative Imagery of Children that synthesizes models for modeling human apparent age and nudity detection. The performance of this approach is thoroughly evaluated on several widely used age estimation and nudity detection datasets. Additionally, preliminary tests were conducted with the help of a local law enforcement agency on a private dataset of SEIC taken from real-world cases with up to 97% accuracy of SEIC video classification. Recent advances in deep learning have led to tremendous achievements in computer vision applications. Specifically for the tasks of automated human age estimation and nudity detection, modern machine learning can predict whether or not an image contains nudity or the presence of a minor with startling accuracy. Fusing together separate models can make possible to identify instances of child pornography without ever coming into contact with the illicit material during model training. (Published Abstract Provided)
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