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. In this paper, a novel framework for automatically identifying Sexually Exploitative Imagery of Children is introduced. 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 \(97\%\) accuracy of SEIC video classification.

深度学习的最新进展在计算机视觉应用中取得了巨大成就。特别是对于自动人类年龄估计和裸露检测任务，现代机器学习可以以惊人的准确度预测图像是否包含裸露或是否存在未成年人。将不同的模型融合在一起可以识别儿童色情的实例，而无需在模型训练期间接触到非法材料。在本文中，介绍了一种用于自动识别儿童性剥削图像的新框架。它是用于模拟人类表观年龄和裸露检测的模型的综合。这种方法的性能在几个广泛使用的年龄估计和裸露检测数据集上进行了全面评估。此外，SEIC视频分类的 \(97\%\)准确率。