Learning multi-level and multi-scale deep representations for privacy image classification

Privacy image classification can help people detect privacy images when people share images. In this paper, we propose a novel method using multi-level and multi-scale features for privacy image classification. We first use CNN (Convolutional Neural Network) to extract multi-levels features. Then, max-pooling layers are employed to obtain multi-scale features at each level. Finally, we propose two feature aggregation models, called Privacy-MSML and Privacy-MLMS to fuse those features for image privacy classification. In Privacy-MSML, multi-scale features of the same level are first integrated and then the integrated features are fused. In Privacy-MLMS, multi-level features of the same scale are first integrated and then the integrated features are fused. Our experiments on a real-world dataset demonstrate the proposed method can achieve better performance compared with the state-of-the-art solutions.