The huge volume of data to be analyzed in the course of child pornography investigations puts special demands on tools and methods for automated classification, often used by law enforcement and prosecution. The need for a clear distinction between pornographic material and inoffensive pictures with a large amount of skin, like people wearing bikinis or underwear, causes problems. Manual evaluation carried out by humans tends to be impossible due to the sheer number of assets to be sighted. The main contribution of this paper is an overview of challenges and limitations encountered in the course of automated classification of image data. An introduction of state-of-the-art methods, including face-and skin tone detection, face-and texture recognition as well as craniofacial growth evaluation is provided. Based on a prototypical implementation of feasible and promising approaches, the performance is evaluated, as well as their abilities and shortcomings.
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