基于迁移学习的暴恐图像自动识别

利用人工智能和深度学习技术自动化地分析互联网海量图片，快速、准确地识别有害的暴恐图像并及时处置是反恐工作的重要手段之一。研究了利用深度学习和迁移学习技术对暴恐图像进行分类识别。首先，定义了暴恐图像的主要概念特征，并针对性地构建数据集；其次，针对暴恐图像正样本较少的问题，设计深度神经网络模型和迁移学习方式；最后，基于构建的训练数据集进行模型训练和测试。结果显示:所提方法可以快速、准确地对互联网图片进行分类识别，平均分类准确率达到96.7%，从而有效降低人工检测的劳动强度，为反恐预警工作提供决策支持。 

Automatic recognition for terrorism related image based on transfer learning

Using AI and deep learning technology to automatically analyze massive Internet pictures, quickly and accurately identifying harmful images related to terrorism and dealing with them in time is one of the important means for anti-terrorism work. This paper studies how to use deep learning and transfer learning technology to classify and recognize the images related to terrorism. First, we define the main concept features of the image related to terrorism and collect the relevant positive samples to construct dataset. Second, we design suitable deep neural network model and transfer learning method for the problem of less positive samples of the image related to terrorism. Finally, using the constructed training dataset to fine-tune the final model. The results show that, based on the proposed method in this paper, we can classify and recognize the Internet pictures which have terrorism content quickly and accurately with average classification accuracy rate of 96.7%, and thus the labor intensity of manual monitoring will reduce effectively, which can provide support for decision-making in the work of anti-terrorism early warning.