KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A Novel System for Detecting Adult Images on the Internet

  • Park, Jae-Yong (Division of Information Management Engineering, Korea University) ;
  • Park, Sang-Sung (Division of Information Management Engineering, Korea University) ;
  • Shin, Young-Geun (Division of Information Management Engineering, Korea University) ;
  • Jang, Dong-Sik (Division of Information Management Engineering, Korea University)
  • Received : 2010.07.29
  • Accepted : 2010.08.29
  • Published : 2010.10.30
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Abstract

As Internet usage has increased, the risk of adolescents being exposed to adult content and harmful information on the Internet has also risen. To help prevent adolescents accessing this content, a novel detection method for adult images is proposed. The proposed method involves three steps. First, the Image Of Interest (IOI) is extracted from the image background. Second, the IOI is distinguished from the segmented image using a novel weighting mask, and it is determined to be acceptable or unacceptable. Finally, the features (color and texture) of the IOI or original image are compared to a critical value; if they exceed that value then the image is deemed to be an adult image. A Receiver Operating Characteristic (ROC) curve analysis was performed to define this optimal critical value. And, the textural features are identified using a gray level co-occurrence matrix. The proposed method increased the precision level of detection by applying a novel weighting mask and a receiver operating characteristic curve. To demonstrate the effectiveness of the proposed method, 2850 adult and non-adult images were used for experimentation.

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References

  1. http://itu.int/, 2010.
  2. Shih J. L., Lee C. H. and Yang C. S., "The Adult Image Identification System Employing Image Retrieval Technique," Pattern Recognition Letters, vol. 28, no. 16, pp. 2367-2374, 2007. https://doi.org/10.1016/j.patrec.2007.08.002
  3. Jinfeng Yang J., Fu Z., Tan T. and Hu W., "A Novel Approach to Detecting Adult Images," in Proc. of the 17th International Conf. on Pattern Recognition, vol. 4, pp. 479-482, 2004.
  4. Hu W., Wu O., Chen Z., Fu Z. and Maybank S., "Recognition Of Pornographic Web Pages By Classifying Texts And Images," IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 29, no. 6, pp. 1019-1034, June 2007. https://doi.org/10.1109/TPAMI.2007.1133
  5. Hammami M., Chahir Y. and Chen L., "WebGuard: A Web Filtering Engine Combining Textual, Structural, and Visual Content-Based Analysis," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 2, pp. 272-284, 2006. https://doi.org/10.1109/TKDE.2006.34
  6. Yong R., Thomas S. Huang and Chang S. F., "Image Retrieval: Current Techniques, Promising Directions, and Open Issues," Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39-62, Mar. 1999. https://doi.org/10.1006/jvci.1999.0413
  7. Imran Shafiq Ahmad, "Text-based Image Indexing and Retrieval using Formal Concept Analysis," KSII Transactions on Internet and Information Systems, vol. 2, no. 3, pp. 150-170, June 2008. https://doi.org/10.3837/tiis.2008.03.002
  8. Smith J. R. and Chang S. F., "Tools and Techniques for Color Image Retrieval," in Proc. of SPIE, Storage and Retrieval for Image and Video Databases IV, vol. 2670, pp. 426-437, Mar. 1996.
  9. Rui Y., Huang T. S. and Mehrotra S., "Relevance Feedback Techniques in Interactive Content-Based Image Retrieval," in Process of SPIE, Storage and Retrieval for Image and Video Databases VI, vol. 3312, pp.25-36, Dec. 1997.
  10. Hermes T., Klauck C., Krey J. W. and Zhang J., "Image Retrieval For Information Systems," in Proc. of SPIE, Storage and Retrieval for Image and Video Databases III, vol. 2420, pp. 394-405, March 1995.
  11. Sakamoto H., Suzuki H. and Uemori A., "Flexible montage retrieval for image data," in Proc. of SPIE, Storage and Retrieval for Image and Video Databases II, vol. 2185, pp. 25-33, 1994.
  12. Xuanjing S., Wei W. and Qingji Q., "The filtering of internet Images Based on Detecting Erotogenic-part," in Proc. of 3rd International Conf. on Natural Computation, ICNC 2007, pp.732-736, Aug. 2007.
  13. Yue W., Jun L., HeeLin W. and ZuJun H., "Automatic Nipple Detection Using Shape and Statistical Skin Color Information," Lecture Notes in Computer Science, vol. 5916, pp. 644-649, 2010.
  14. Mojsilovic' A., Kova'cevic' J., Hu J., Safranek R. J. and Ganapathy S. K., "Matching and Retrieval Based on the Vocabulary and Grammar of Color Patterns," IEEE Transactions on Image Processing, vol. 9, no. 1, pp. 38-54, Jan. 2000. https://doi.org/10.1109/83.817597
  15. Kang S. D., Park S. S., Yoo H. W., Shin Y. G. and Jang D. S., "Development of expert system for extraction of the objects of interest," Expert Systems with Applications, vol. 36, no. 3, pp. 7210-7218, Apr. 2009. https://doi.org/10.1016/j.eswa.2008.09.062
  16. Ha Y. C., "Nature Picture and Human," Donga Press, Seoul, Korea, 2002.
  17. Yoo H. W, Jung S. H., Jang D. S. and Na. Y. K., "Extraction of Major Object Feature Using VQ Clustering for Content-Based Image Retrieval," Pattern Recognition, vol. 35, no. 5, pp. 1115-1126, May 2002. https://doi.org/10.1016/S0031-3203(01)00105-4
  18. Wang H. and Chang S. F., "A highly efficient system for automatic face region detection in MPEG video," IEEE Transactions on Circuits and System for Video Technology, vol. 7, no. 4, pp. 615-628, Aug. 1997. https://doi.org/10.1109/76.611173
  19. Garcia C. and Tziritas G., "Face detection using quantized skin color regions merging and wavelet packet analysis," IEEE Transactions on Multimedia, vol. 1, no. 3, pp. 264-277, Sept. 1999. https://doi.org/10.1109/6046.784465
  20. Robert M. Haralick, K. Shanmugam and I. Dinstein, "Textural Features for Image Classification," IEEE Transactions on Systems, man and Cybernetics, vol. 3, no. 6, pp. 610-621, Nov. 1973.
  21. Weszka J. S., Dyer C. R. and Rosenfeld A., "A comparative study of texture measures for terrain classification," IEEE Transactions on Systems, man and Cybernetics, vol. 6, pp. 269-285, 1979.
  22. Zhang Y., "Optimisation of building detection in satellite images by combining multispectral classification and Texture filtering," ISPRS Journal of Photogrammetry & Remote Sensing, vol. 54, no. 1, pp. 50-60, Feb. 1999. https://doi.org/10.1016/S0924-2716(98)00027-6
  23. Franklin S. E., Wulder M. A. and Gerylo G. R., "Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia," International Journal of Remote Sensing, vol. 22, no. 13, pp. 2627-2632, 2001. https://doi.org/10.1080/01431160120769
  24. Forsyth D. A. and Fleck M. M., "Body plans," IEEE Conf. on Computer Vision and Pattern Recognition, pp. 678-683, 1997.
  25. Wang J. Z., Li J., Wiederhold G. and Firschein O., "System for screening objectionable images," Computer Communications, vol. 21, no. 15, pp. 1355-1360, Oct. 1998. https://doi.org/10.1016/S0140-3664(98)00203-5
  26. Yang J., Fu Z., Tan T. and Hu W., "A novel approach to detecting adult images," in Proc. of the 17th International Conf. on Pattern Recognition, vol. 4, pp. 479-482, Aug. 2004.
  27. Zeng W., Gao W., Zhang T. and Liu Y., "Image Guarder: An Intelligent Detector for Adult," in Proc. of Asian Conf. on Computer Vision, Jeju Island, South Korea, pp. 198-203, Jan. 2004.
  28. Zhu Q., Wu C. T., Cheng K. T. and Wu Y. L., "An Adaptive Skin Model and Its Application to Objectionable Image Filtering," in Proc. of the 12th annual ACM International Conf. on Multimedia, New York, USA, pp. 56-63, 2004.

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