With the rapid development of Internet technology, sensitive content images have changed from basic concealed content exchange to mass data sharing. The traditional method of sensitive content detection based on image feature extraction is no longer applicable. To overcome these difficulties, this paper proposed a sensitive content detection method based on sparse semantics and double-layer deep convolution neural network. In this method, the upper network preprocessed the training samples and constructed sparse semantic representation of the image as the input of the neural network, while the lower network further considered the third-party control mechanism(such as government agents) and proposed a sensitive content image detection method for specific groups. Compared with the existing image detection methods for sensitive content, this method can effectively reduce the number of training samples, and the detection accuracy is more than 7% higher than that of traditional image detection methods(such as visual word bag method).

[2020-05-05] Printed Article /doc/2018.11.0851/v2