Categorization of spam images and identification of controversial images on mobile phones using machine learning and predictive learning

References

• Whatsapp revenue and usage statistics. http://www.businessofapps.com/data/whatsapp-statistics/, accessed: 2018-08-07 (2017).
   Google Scholar
• Whatsapp users share 55 billion texts, 4.5 billion photos, 1 billion videos daily. https://www.businesstoday.in/technology/news/whatsapp-users-share-texts-photos-videos-daily/story/257230. html, accessed: 2018-08-06
   Google Scholar
• Aiwan, F., Zhaofeng, Y.: Image spam filtering using convolutional neural networks. Personal Ubiquitous Computing 22(5-6), 1029–1037 (Oct 2018) doi: 10.1007/s00779-018-1168-8
   Web of Science ®Google Scholar
• Amos, B., Ludwiczuk, B., Satyanarayanan, M., et al.: Openface: A general-purpose face recognition library with mobile applications. CMU School of Computer Science (2016)
   Google Scholar
• Biggio, B., Fumera, G., Pillai, I., Roli, F.: Image spam filtering by content obscuring detection. In: CEAS (2007)
   Google Scholar
• Bradski, G., Kaehler, A.: Learning OpenCV: Computer vision with the OpenCV library.” O’Reilly Media, Inc.” (2008)
   Google Scholar
• Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. vol. 1, pp. 886–893. IEEE (2005)
   Google Scholar
• Fumera, G., Pillai, I., Roli, F., Biggio, B.: Image spam filtering using textual andvisual information. In: Proceedings of the MIT Spam Conference 2007 (2007)
   Google Scholar
• Gao, Y., Choudhary, A., Hua, G.: A comprehensive approach to image spam detection: from server to client solution. IEEE Transactions on Information Forensics and Security 5(4), 826–836 (2010) doi: 10.1109/TIFS.2010.2080267
   Web of Science ®Google Scholar
• Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1867–1874 (2014)
   Google Scholar
• Ke, Y., Sukthankar, R., Huston, L., Ke, Y., Sukthankar, R.: Efficient near-duplicatedetection and sub-image retrieval. In: Acm Multimedia. vol. 4, p. 5. Citeseer (2004)
   Google Scholar
• Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations. pp. 55–60 (2014), http://www.aclweb.org/anthology/P/P14/P14-5010 doi: 10.3115/v1/P14-5010
   Google Scholar
• Ng, T.T., Chang, S.F., Tsui, M.P.: Lessons learned from online classification of photo-realistic computer graphics and photographs. In: Signal Processing Applications for Public Security and Forensics, 2007. SAFE’07. IEEE Workshop on. pp. 1–6. IEEE (2007)
   Google Scholar
• Sachdeva, A., Kapoor, R., Sharma, A., Mishra, A.: Categorical classification and deletion of spam images on smartphones using image processing and machine learning. In: Machine Learning and Data Science (MLDS), 2017 International Conference on. pp. 23–30. IEEE (2017)
   Google Scholar
• Shang, E.X., Zhang, H.G.: Image spam classification based on convolutional neural network. In: Machine Learning and Cybernetics (ICMLC), 2016 International Conference on. vol. 1, pp. 398–403. IEEE (2016)
   Google Scholar
• Smith, R.: An overview of the tesseract ocr engine. In: Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on. vol. 2, pp. 629–633. IEEE (2007)
   Google Scholar
• Su, C.Y., Shen, D.F., Lin, G.S.: An image spam detection method. In: Consumer Electronics-Taiwan (ICCE-TW), 2017 IEEE International Conference on. pp. 71–72. IEEE (2017)
   Google Scholar
• Wang, Z., Josephson, W.K., Lv, Q., Charikar, M., Li, K.: Filtering image spam with near-duplicate detection. In: CEAS (2007)
   Google Scholar
• Wang, Z., Bovik, A.C.: Mean squared error: Love it or leave it? a new look at signal fidelity measures. IEEE signal processing magazine 26(1), 98–117 (2009) doi: 10.1109/MSP.2008.930649
   Web of Science ®Google Scholar
• Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004) doi: 10.1109/TIP.2003.819861
   PubMed Web of Science ®Google Scholar
• Yuan, S., Zhang, C.: An improved multiple features fusion method for image spam filtering. In: Information Science and Control Engineering (ICISCE), 2016 3rd International Conference on. pp. 200–203. IEEE (2016)
   Google Scholar
• Zhang, Y., Zhang, Y., Sun, J., Li, H., Zhu, Y.: Learning near duplicate image pairs using convolutional neural networks. International Journal of Performability Engineering 14(1), 168 (2018)
   Google Scholar
• Zhou, Z., Wang, Y., Wu, Q.J., Yang, C.N., Sun, X.: Effective and efficient global context verification for image copy detection. IEEE Transactions on Information Forensics and Security 12(1), 48–63 (2017) doi: 10.1109/TIFS.2016.2601065
   Web of Science ®Google Scholar
• YI Lihamu, YA Ermaimaiti (2018) A new Uyghur face recognition algorithm, Journal of Interdisciplinary Mathematics, 21:5,1055-1060, DOI: 10.1080/09720502.2018.1493029
   Web of Science ®Google Scholar