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Over the last decade, advancements in deep learning and computer vision have led to a tremendous growth in performance at the tasks of automated human age estimation and nudity detection. Modern machine learning models can predict whether or not an image contains nudity or the presence of a minor with startling accuracy. When used in conjunction, these technological advancements can be used to identify new instances of child pornography without ever coming into contact with the illicit material during model training. In this thesis, a label distribution learning framework for modeling human apparent age is proposed. Instead of directly modeling a person’s biological age, we use a probability distribution over a sample of humans guessing how old that person looks like as the ground truth. This allows us to better capture the subjective nature of a person’s age and advance state of the art performance at the task of apparent age estimation. Next, we introduce a framework to automatically identify Sexually Exploitative Imagery of Children (SEIC) in both images and video. It is a synthesis of our original age estimation models and Yahoo!’s open sourced nudity detection model, OpenNSFW. Deep learning models are used to identify the presence of a minor or nudity in any given image or video. The performance of this approach is 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.

Deep Learning of Human Apparent Age for the Detection of Sexually Exploitative Imagery of Children