Prototype Pemblokir Gambar Pornografi Menggunakan Algoritma Viola and Jones

Muhammad Fikri Hidayattullah, Miftakhul Huda

Abstract


The spread of pornographic content extends very quickly through internet media. Various types of porn sites increase dramatically every day. Various prevention efforts to overcome the spread of pornographic content have been carried out. Starting from URL blocking, text filtering to be more specific directly to pornographic images through skin detection. However, all of these methods have various weaknesses. An effective step to reduce the spread of pornographic images is to block the breast area that is characteristic of the pornographic images. In this study a breast area blocking prototype was developed on pornographic images using the Viola and Jones algorithm.. From the test results obtained a precision value of 82.61%, 77.55% recall and accuracy of 78.26%.


Full Text:

References


P. F. Fagan and D. Ph, “The Effects of Pornography on Individuals, Marriage, Family, and Community,” Marriage & Religion Institute, pp. 1–26, 2009.

B. J. Ropelato, “Internet Pornography Statistics,” pp. 1–10, 2014.

EDsmart, “Internet Pornography Stats,” Pornography by the Numbers, 2015. .

S. N. Hamade, “Internet Filtering and Censorship,” pp. 1082–1087, 2008.

J. Zhang, P. Drive, and J. Qin, “The Role of URLs in Objectionable Web Content Categorization 1,” 2006.

S. Sen, “Adult Website Classifier Url Text Classification Url Features,” 2010.

Z. Chen, O. Wu, M. Zhu, and W. Hu, “A Novel Web Page Filtering System by Combining Texts and Images,” no. 95, pp. 0–3, 2006.

Z. Gao, G. Lu, H. Dong, S. Wang, H. Wang, and X. Wei, “Pornographic Web Filtering,” pp. 270–273, 2008.

Y. Lin, H. Tseng, and C. Fuh, “Pornography Detection Using Support Vector Machine,” 16th IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP 2003), vol. 19, no. Cvgip, pp. 123–130, 2003.

J. I. Agbinya, B. Lok, Y. S. Wong, and S. Da Silva, “Automatic Online Porn Detection and Tracking,” Faculty of Engineering, University of Technology, Sydney, 1 Broadway, Sydney 2007, 2007.

X. Wang, C. Hu, and S. Yao, “A Breast Detecting Algorithm for Adult Image Recognition,” pp. 341–344, 2009.

X. Shen, W. Wei, and Q. Qian, “The Filtering of Internet Images Based on Detecting Erotogenic-part,” no. Icnc, pp. 3–7, 2007.

Y. Wang, J. Li, H. Wang, and Z. Hou, “Automatic Nipple Detection Using Shape and Statistical Skin Color Information,” Proceeding MMM’10 Proceedings of the 16th international conference on Advances in Multimedia Modeling Pages 644-649 Springer-Verlag Berlin, Heidelberg ©2010, pp. 644–649, 2010.

X. Kejun and W. Jian, “Automatic Nipple Detection Using Cascaded AdaBoost Classifier,” vol. 1, no. 3, pp. 1–6, 2012.

V. Thaweekote, P. Songram, and C. Jareanpon, “Automatic Nipple Detection based on Face Detection and Ideal Proportion Female using Random Forest,” no. C, pp. 11–15, 2013.

P. Viola, “Rapid Object Detection using a Boosted Cascade of Simple Features,” Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, no. 2001, pp. I–511 – I–518 vol.1, 2001.

C. P. Papageorgiou, M. Oren, and T. Poggio, “A general framework for object detection,” in Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 1998, pp. 555–562.

R. Lienhart and J. Maydt, “An extended set of Haar-like features for rapid object detection,” Proceedings. International Conference on Image Processing, vol. 1, pp. I–900–I–903, 2002.




DOI: https://doi.org/10.30591/jpit.v4i1.1035

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

JPIT INDEXED BY