Image has become a main medium of Internet information dissemination, makes it easy for an Internet visitor to get pornographic images with just few clicks on websites. It is necessary to build pornographic image recognition systems since uncontrolled spreading of adult content could be harm to the adolescents. Previous solutions for pornographic image recognition are usually based on hand-crafted features like human skin color. Hand-crafted feature based methods are straightforward to understand and use but limited in specific situations. In this paper, we propose a deep learning based approach with multiple feature fusion transfer learning strategy. Firstly, we obtain the training data from an open data set called NSFW with 120,000+ images. Images would be classified into different levels according to its content sensitivity. Then we employ data augment methods, train a deep convolutional neural network to extract image features and conduct the classification job, without the need for hand-crafted rules. A pre-trained model is used to initialize the network and help extract the basic features. Furthermore, we propose a fusion method that makes use of multiple transfer learning models in inference, to improve the accuracy on the test set. The experimental results prove that our method achieves high accuracy on the pornographic image recognition and inspection task.

图像已成为Internet信息传播的主要媒介，它使Internet访问者只需在网站上单击几下即可轻松获得色情图像。有必要建立色情图像识别系统，因为成人内容的不受控制的传播可能会对青少年造成伤害。色情图像识别的先前解决方案通常基于手工制作的功能，例如人类肤色。基于手工特征的方法易于理解和使用，但在特定情况下受到限制。在本文中，我们提出了一种基于深度学习的方法，其中包含多特征融合转移学习策略。首先，我们从一个称为NSFW的开放数据集中获取训练数据，其中包含120,000多个图像。图像将根据其内容敏感度分为不同级别。然后，我们采用数据扩充方法，训练一个深度卷积神经网络来提取图像特征并进行分类工作，而无需手工制定规则。预训练模型用于初始化网络并帮助提取基本特征。此外，我们提出了一种融合方法，该方法利用推理中的多个转移学习模型来提高测试集的准确性。实验结果证明，该方法在色情图像的识别和检查任务中具有较高的准确性。我们提出一种融合方法，该方法利用推理中的多个转移学习模型来提高测试集的准确性。实验结果证明，该方法在色情图像的识别和检查任务中具有较高的准确性。我们提出一种融合方法，该方法利用推理中的多个转移学习模型来提高测试集的准确性。实验结果证明，该方法在色情图像的识别和检查任务中具有较高的准确性。