This article presents a novel framework for using artificial intelligence to identify instances of child pornography without human examination of the illicit material during model training.
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 it possible to identify instances of child pornography without ever coming into contact with the illicit material during model training. In this paper, the authors introduce a novel framework for automatically identifying Sexually Exploitative Imagery of Children. It is a synthesis of 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 percent accuracy of SEIC video classification. Publisher Abstract Provided