Inappropriate Visual Content Detection Based on the Joint Training Strategy

In the information age, massive Internet data brings convenience to us. But there is some inappropriate visual content (pornography, violence, politics, terrorism, etc.), among which the dissemination of pornographic content has an adverse influence, especially for children and minors. Therefore, we present an inappropriate visual content detection method based on the joint training strategy in an end-to-end manner, which realizes the identification and location of inappropriate visual content while retaining the base class (80 categories in the COCO dataset) detection. To solve the difficulty of sample labeling, in this paper we propose a combined training strategy of detection and classification. And the Focal loss is used to improve the sample imbalance in the network sharing training. The algorithm can achieve multi-label output and has good recognition accuracy. Finally, a more challenging dataset INVC of inappropriate visual content is proposed, which includes three types of sample data in complex backgrounds at different scales, such as indoor, beach, street, etc.