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As a result of the rapid development of internet technology, images are widely used on various social networks, such as WeChat, Twitter or Facebook. It follows that images with spam can also be freely transmitted on social networks. Most of the traditional methods can only detect spam in the form of links and texts; there are few studies on detecting images with spam. To this end, a novel detection method for identifying social images with spam, based on deep neural network and frequency domain pre-processing, is proposed in this paper. Firstly, we collected several images with embedded spam and combined the DIV2K2017 dataset to build an image dataset for training the proposed detection model. Then, the specific components of the spam in the images were determined through experiments and the pre-processing module was specially designed. Low-frequency domain regions with less spam are discarded through Haar wavelet transform analysis. In addition, a feature extraction module with special convolutional layers was designed, and an appropriate number of modules was selected to maximize the extraction of three different high-frequency feature regions. Finally, the different high-frequency features are spliced along the channel dimension to obtain the final classification result. Our extensive experimental results indicate that the spam element mainly exists in the images as high-frequency information components; they also prove that the proposed model is superior to the state-of-the-art detection models in terms of detection accuracy and detection efficiency.

A Detection Method for Social Network Images with Spam, Based on Deep Neural Network and Frequency Domain Pre-Processing