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Challenges and Limitations Concerning Automatic Child Pornography Classification

机译：关于自动儿童色情分类的挑战和限制

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摘要

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.
机译：在儿童色情调查过程中要分析的大量数据对自动分类的工具和方法进行了特别要求，通常由执法和起诉使用。需要清楚地区分色情材料和患有大量皮肤的无福洁的照片，如人们穿着自由基语或内衣，导致问题。由于庞大的资产被视为庞大的资产，人类进行的手动评估往往是不可能的。本文的主要贡献概述了图像数据自动分类过程中遇到的挑战和局限性。提供了最先进的方法，包括面部和肤色检测，面部和纹理识别以及颅面和颅面生长评估。根据可行和有前途的方法的原型实施，评估性能，以及他们的能力和缺点。

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