**4. Conclusions**

In this paper, a detection method is proposed for identifying social media images containing spam, based on a deep neural network and frequency domain pre-processing. Our research contributions can be summarized as follows:


Unlike the current detection models, this paper first verifies the specific components of spam in the image and then designs a more targeted detection framework, which can enhance the detection efficiency and accuracy of the proposed model. In future work, we will further expand the created image dataset and improve the recognition ability and efficiency of the proposed model. In addition, although the proposed model demonstrates good detection performance on fixed image datasets, it lacks breadth, which will be addressed. Improving the applicability of the model is another future research focus.

**Author Contributions:** H.S.: conceptualization, methodology, data preprocessing; data analysis, writing-original draft preparation; X.L.: data collection, writing-review and editing, visualization; X.Z.: conceptualization, supervision, project administration. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (61272374, 61300190), the Key Project of the Chinese Ministry of Education (313011), and the Foundation of the Department of Education of Liaoning Province (L2015001).

**Acknowledgments:** We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.
