青少年在社交网络上浏览色情图片比较容易.研究者对真实色情图片的检测进行了大量研究,但是对人造色情图片的研究较少.本文主要针对社交网络中人造色情图片的识别进行了研究.整个过程由两部分组成:特征选择和图像识别.在特征选择中,本文选择了7大类型的特征.在图像识别中,主要包括3个步骤.1)为了缓解正、副样本的不平衡数据,本文对人造色情图片的样本集进行了扩充.2)提出了一种特征快速提取的方法.3)对比了三种类型的树模型,并选择了其中效果最好的GBDT模型作为最终图片识别的模型.通过实验验证了本文方法能够获得较好的精度,同时耗时较短.%It is easy for teenagers to view pornographic pictures on social networks. Many researchers have studied the detection of real pornographic pictures, but there are few studies on those that are artificial. In this work, we studied how to detect artificial pornographic pictures, especially when they are on social networks. The whole detection process can be divided into two stages: feature selection and picture detection. In the feature selection stage, seven types of features that favour picture detection were selected. In the picture detection stage, three steps were included. 1) In order to alleviate the imbalance in the number of artificial pornographic pictures and normal ones, the training dataset of artificial pornographic pictures was expanded. Therefore, the features which were extracted from the training dataset can also be expanded too. 2) In order to reduce the time of feature extraction, a fast method which extracted features based on the proportionally scaled picture rather than the original one was proposed. 3) Three tree models were compared and a gradient boost decision tree (GBDT) was selected for the final picture detection. Three sets of experimental results show that the proposed method can achieve better recognition precision and drastically reduce the time cost of the method.
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