1. Advanced Techniques for Automatic Web Filtering
James Z. Wang
PNC Tech. Career Dev. Professor
Penn State University
Joint Work: Jia Li, Assist. Prof., Penn State Statistics
Gio Wiederhold, Prof., Stanford Computer Science
11/16/2018
J. Z. Wang, Penn State University

2. J. Z. Wang, Penn State University
Outline
The problem
Related approaches
Filtering based on image content
Goals and methods
The WIPE system
Experimental results
Website classification by image content
Conclusions and future work
11/16/2018
J. Z. Wang, Penn State University

3. The Size and Content of the Web
02/99: ~16 million total web servers
Estimated total number of pages on the web:
~800 million
15 Terabytes of text
(comparable to text of Library of Congress)
Year 2001: 3 to 5 billion pages
Lawrence, Giles, Nature, 1999.
11/16/2018
J. Z. Wang, Penn State University

4. J. Z. Wang, Penn State University
Outline
The problem
Related approaches
Filtering based on image content
Goals and methods
The WIPE system
Experimental results
Website classification by image content
Conclusions and future work
11/16/2018
J. Z. Wang, Penn State University

5. Pornography-free Websites
E.g. Yahoo!Kids, disney.com
Useful in protecting those children too young to know how to use the Web browser
It is difficult to control access to other sites
11/16/2018
J. Z. Wang, Penn State University

6. J. Z. Wang, Penn State University
Text-based Filtering
E.g. NetNanny, Cyber Patrol, CyberSitter
Methods:
Store more than 10,000 IPs
Blocking based on keywords
Block all image access
Problems:
Internet is dynamic
Keywords are not enough (e.g. text incorporated in images)
Images are needed for all net users
11/16/2018
J. Z. Wang, Penn State University

7. Classification of Web Community
Flake, Lawrence, Giles, ACM KDD, 2000
Graph clustering based on max flow – min cut analysis of the Web connectedness
11/16/2018
J. Z. Wang, Penn State University

8. J. Z. Wang, Penn State University
Outline
The problem
Related approaches
Filtering based on image content
Goals and methods
The WIPE system
Experimental results
Website classification by image content
Conclusions and future work
11/16/2018
J. Z. Wang, Penn State University

9. J. Z. Wang, Penn State University
Goals and Methods
The problem comes from images, we deal with images
Goals: use machine learning and image retrieval to classify Web images and Websites
Requirements: high accuracy and high speed
Challenges: non-uniform image background, textual noise in foreground, wide range of image quality, wide range of camera positions, wide range of composition…
11/16/2018
J. Z. Wang, Penn State University

10. J. Z. Wang, Penn State University
The WIPE System
Inspired by the UC Berkeley’s FNP System
Detailed analysis of images
Skin filter and human figure grouper
Speed: 6 mins CPU time per image
Accuracy: 52% sensitivity and 96% specificity
Stanford WIPE System
Wavelet-based feature extraction + image classification + integrated region matching + machine leaning
Speed: < 1 second CPU time per image
Accuracy: 96% sensitivity and 91% specificity
11/16/2018
J. Z. Wang, Penn State University

11. J. Z. Wang, Penn State University
System Flow
Original Web Image
Feature Extraction
(color, texture, shape)
Type Classification
photograph
Photo Classification
Result: REJECT or PASS
Training Features
11/16/2018
J. Z. Wang, Penn State University

12. J. Z. Wang, Penn State University
Wavelet Principle
11/16/2018
J. Z. Wang, Penn State University

13. J. Z. Wang, Penn State University
Type Classification
Graphs:
Manually-generated images with smooth tones.
11/16/2018
J. Z. Wang, Penn State University

14. J. Z. Wang, Penn State University
Type Classification
Photographs:
Images with continuous tones.
11/16/2018
J. Z. Wang, Penn State University

15. J. Z. Wang, Penn State University
Photo Classification
Content-based image retrieval
+ statistical classification
11/16/2018
J. Z. Wang, Penn State University

16. J. Z. Wang, Penn State University
Experimental Results
Tested on a set of over 10,000 photographic images
Speed: Less than one second of response time on a Pentium III PC
Accuracy
Type of Images
Test + (Rejected)
Test – (Passed)
Objectionable
96% 4%
Benign 9%
91%
11/16/2018
J. Z. Wang, Penn State University

17. J. Z. Wang, Penn State University
Comment on Accuracy
The algorithm can be adjusted to trade off specificity for higher sensitivity
In a real-world filtering application system, both the sensitivity and the specificity are expected to be higher
Icons and graphs can be classified with almost 100% accuracy  higher specificity
Combine text and image classification  higher sensitivity and higher speed
11/16/2018
J. Z. Wang, Penn State University

18. False Classifications Benign Images
Partially obscured human
Areas with similar features
Painting, fine-art
Partially undressed human
Animals (w/o clothes)
11/16/2018
J. Z. Wang, Penn State University

19. False Classifications Objectionable Images
Partially dressed
Undressed area
too small
Dressed but objectionable
Frame and text noise
Dark, low contrast
11/16/2018
J. Z. Wang, Penn State University

20. Website Classification by Image Content
An objectionable site will have many such images
For a given objectionable Website, we denote p as the chance of an image on the Website to be an objectionable image
p is the percentage of objectionable images over all images provided by the site
We assume some distributions of p over all Websites (e.g., Gaussian, shifted Gaussian)
Classification levels could be provided as a service to filtering software producers
11/16/2018
J. Z. Wang, Penn State University

21. Flow in Website classification
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J. Z. Wang, Penn State University

22. Website Classification
Based on statistical analysis (see paper), we know we can expect higher than 97% accuracy on Website classification if
We download images for each site
We classify a Website as objectionable if 20-25% of downloaded images are objectionable
Using text and IP addresses as criteria, the accuracy can be further improved
skip IPs for museums, dog-shows, beach towns, sport events
11/16/2018
J. Z. Wang, Penn State University

23. J. Z. Wang, Penn State University
Outline
The problem
Related approaches
Filtering based on image content
Goals and methods
The WIPE system
Experimental results
Website classification by image content
Conclusions and future work
11/16/2018
J. Z. Wang, Penn State University

24. Conclusions and Future Work
Perfect filtering is never possible
Effective filtering based on image content is feasible with the current technology
Systems that combine content-based filtering with text-based criteria will have good accuracy and acceptable speed
Objectionable websites are automatically identifiable, a service for the community?
The technology can still be improved through further research.
11/16/2018
J. Z. Wang, Penn State University

25. J. Z. Wang, Penn State University
References
(papers)
... /cgi-bin/zwang/wipe2_show.cgi (demo)
... /pub/gio/inprogress.html#COPA (testimony)
(James Wang)
(Gio Wiederhold)
(Michel Bilello)
11/16/2018
J. Z. Wang, Penn State University