ABSTRACT To improve the ability of sensitive information recognition and social network big data analysis, a social network image sensitive information recognition processing method, in view of the deep residual network, is proposed. The construction of a block-matching model for the distribution region of sensitive information in social network images facilitates the fusion filtering processing of sensitive information through the implementation of the deep residual network block-matching technique. On this basis, the spatial domain visual features of social network images are extracted. The adaptive enhancement method of the sensitive information region in the histogram interface is used to balance the sensitive information, and the balanced configuration model of the sensitive information of social network images is established. The experimental results demonstrated that the method could effectively identify the sensitive information in social network images. The image signal-to-noise ratio was measured at 100 dB, and the maximum recognition time was recorded at 2.36 s, indicating a satisfactory recognition effect. This research provides a new technical means for the rapid recognition and processing of sensitive information in social network images, which has important theoretical and practical significance.
Xu et al. (Tue,) studied this question.