A Forensic Nudity Detector Based on Machine Learning

• Danilo Coura Moreira UFCG
• Joseana Macêdo Fechine UFCG

DOI: https://doi.org/10.5753/sbseg.2018.4258

Abstract

Forensic examinations on computer devices become increasingly costly due to the large amount of files they can store. It is necessary to use algorithms that could detect pornographic images efficiently. We presented an approach that improved the work of Ap-Apid using a machine learning-based algorithm rather than using static rules. In addition, we used features extracted from a face detector. The AIIA-PID4 pornographic data set was used in the experimental phase. In the end, the proposed model reached an accuracy of 93.56%, outperforming the referenced works, which reached 79.1% and 85.05%, respectively.

References

Ap-Apid, R. (2005). An algorithm for nudity detection. In Proceedings of the 5th Philippine Computing Science Congress, Cebu City, Philippines.

Basilio, J. A. M., Torres, G. A., Pérez, G. S., Medina, L. K. T., and Meana, H. M. P. (2011). Explicit image detection using ycbcr space color model as skin detection. In Proceedings of the 2011 American Conference on Applied Mathematics and the 5th WSEAS International Conference on Computer Engineering and Applications, AMERICAN-MATH’11/CEA’11, pages 123–128, Stevens Point, Wisconsin, USA. World Scientific and Engineering Academy and Society (WSEAS).

Breiman, L. (2001). Random forests. Mach. Learn., 45(1):5–32.

Cappellari, M. S. V. (2005). A pedofilia na pós-modernidade: um problema que ultrapassa a cibercultura. Em Questão, 11:67–82.

Eleutério, P. M. D. S. and Machado, M. P. (2011). Desvendando a computação forense. Novatec, São Paulo, 1 edition.

Haykin, S. S. (2009). Neural networks and learning machines. Pearson Education, Upper Saddle River, NJ, third edition.

Hosmer, D. W. and Lemeshow, S. (2000). Applied logistic regression. John Wiley and Sons.

Karavarsamis, S., Ntarmos, N., Blekas, K., and Pitas, I. (2013). Detecting pornographic images by localizing skin rois. International Journal of Digital Crime and Forensics (IJDCF), 5:39–53.

Khan, R., Hanbury, A., Stöttinger, J., and Bais, A. (2012). Color based skin classification. Pattern Recognition Letters, 33(2):157 – 163.

Kovac, J., Peer, P., and Solina, F. (2003). Human skin color clustering for face detection. In The IEEE Region 8 EUROCON 2003. Computer as a Tool., volume 2, pages 144–148 vol.2.

Lucena, O., Oliveira, . D. P., Veloso, L., and Pereira, E. (2017). Improving face detection performance by skin detection post-processing. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 300–307.

Ma, B., Zhang, C., Chen, J., Qu, R., Xiao, J., and Cao, X. (2014). Human skin detection via semantic constraint. In Proceedings of International Conference on Internet Multi-media Computing and Service, ICIMCS ’14, pages 181:181–181:184, New York, NY, USA. ACM.

Mahmoodi, M. R. and Sayedi, S. (2016). A comprehensive survey on human skin detection. International Journal of Image, Graphics and Signal Processing, 8:1–35.

Medina, M. R. and Palladino, P. (2017). Pornographic images jacking algorithm. [link]. Accessed: November 17, 2017.

Muhammad, B. and Abu-Bakar, S. A. R. (2015). A hybrid skin color detection using hsv and ycgcr color space for face detection. In 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pages 95–98.

Oghaz, M. M., Maarof, M. A., Zainal, A., Rohani, M. F., and Yaghoubyan, S. H. (2015). A hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique. PLOS ONE, 10(8):1–21.

Patil, H. Y., Bharambe, S. V., Kothari, A. G., and Bhurchandi, K. M. (2013). Face localization and its implementation on embedded platform. In 2013 3rd IEEE International Advance Computing Conference (IACC), pages 741–745.

Platzer, C., Stuetz, M., and Lindorfer, M. (2014). Skin sheriff: A machine learning solution for detecting explicit images. In Proceedings of the 2Nd International Workshop on Security and Forensics in Communication Systems, SFCS ’14, pages 45–56, New York, NY, USA. ACM.

Polastro, M. d. C. and Eleutério, P. M. d. S. (2010). Nudetective: A forensic tool to help combat child pornography through automatic nudity detection. In 2010 Workshops on Database and Expert Systems Applications, pages 349–353.

Putro, M. D., Adji, T. B., and Winduratna, B. (2015). Adult image classifiers based on face detection using viola-jones method. In 2015 1st International Conference on Wireless and Telematics (ICWT), pages 1–6.

Vezhnevets, V., Sazonov, V., and Andreeva, A. (2003). A survey on pixel-based skin color detection techniques. In IN PROC. GRAPHICON-2003, pages 85–92.

Viola, P. and Jones, M. J. (2004). Robust real-time face detection. Int. J. Comput. Vision, 57(2):137–154.

Xiong, W. and Li, Q. (2012). Chinese skin detection in different color spaces. In 2012 International Conference on Wireless Communications and Signal Processing (WCSP), pages 1–5.

Yang, J., Shi, Y., and Xiao, M. (2007). Geometric feature-based skin image classification. In Huang, D.-S., Heutte, L., and Loog, M., editors, Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, pages 1158–1169, Berlin, Heidelberg. Springer Berlin Heidelberg.

Youtian, D., Zhongmin, C., Xiaohong, G., and Qian, L. (2012). Almost optimal skin detection approach within the gaussian framework. Optical Engineering, 51:51 – 51 – 10.

Zafeiriou, S., Zhang, C., and Zhang, Z. (2015). A survey on face detection in the wild: Past, present and future. Computer Vision and Image Understanding, 138:1 – 24.

Zhou, K., Zhuo, L., Geng, Z., Zhang, J., and Li, X. G. (2016). Convolutional neural networks based pornographic image classification. In 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), pages 206–209.