1. A Tool for Implementing COPA+ (Child Online Protection Act)
James Z. Wang & Gio Wiederhold,
Penn State University. Inf.Sc. / Stanford University, CSD
Joint Work: Jia Li, Penn State Statistics
wang.ist.psu.edu / www-db.stanford.edu/IMAGE
www-db.stanford.edu/pub/gio/inprogress.html#COPA
11/16/2018
J. Z. Wang & Gio Wiederhold

2. J. Z. Wang & Gio Wiederhold
Outline
The Issues: legal and community pressures
Current approaches to protect kids
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 & Gio Wiederhold

3. Status of legal attempts to restrict dissemination of porn to minors:
CDA: Communications Decency Act of Restricts Transmission of Porn. Overturned for being overly restrictive of the rights of adults by Philadelphia district court; decision upheld by Supreme court in 1997.
COPA: Child Online Protection Act of Fines to ISPs for delivering porn to minors. Again overturned for being overly restrictive of the rights of adults in implementation, by Philadelphia district court, decision upheld by appeals, now before Supreme court. NRC study.
CIPA: Children's Internet Protection Act , passed late 2000, requires schools and libraries to install filtering software on all Internet-connected computers to screen out pornographic images as a condition of receiving federal funding. The law goes into effect April 20, but a suit is being brought again to the Philadelphia court. Regulations giving the specifics of how to comply to be issued by the Federal Communications Commission ( ) in late March 2001.
The suits were/are filed by the ACLU and the ALA (Am.Library Ass.).
Other participants in the arguments include the porn-industry, religious and parental organizations, the FBI, and filtering technology providers
11/16/2018
J. Z. Wang & Gio Wiederhold

4. 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]
Frequency of access and search
#2, after music [Google]
11/16/2018
J. Z. Wang & Gio Wiederhold

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 & Gio Wiederhold

6. J. Z. Wang & Gio Wiederhold
Filtering Software
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, especially porn sites
Keywords are not satisfactory
text hidden incorporated in images
Excessive filtering (Anne Sexton, cum laude, breast cancer)
Images are needed for all net users
Poor reputation, poor sales, no funds to improve
11/16/2018
J. Z. Wang & Gio Wiederhold

7. Image based-filtering
The problem comes from images!
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…
Our approach: rapid feature extraction, machine learning of patterns, fast matching
Applications: classify Web images and Websites
11/16/2018
J. Z. Wang & Gio Wiederhold

8. The WaveletImagePornographyElimination 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 (medical image analysis spinoff)
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 & Gio Wiederhold

9. J. Z. Wang & Gio Wiederhold
System Flow
Source Web Image
Feature Extraction
(color, texture, shape)
Training
Feature Extraction
(color, texture, shape)
Type Classification
photograph
graph
Features
from
Training
Photo Classification
Result: REJECT or PASS
11/16/2018
J. Z. Wang & Gio Wiederhold

10. J. Z. Wang & Gio Wiederhold
Wavelet Principle
11/16/2018
J. Z. Wang & Gio Wiederhold

11. J. Z. Wang & Gio Wiederhold
Type Classification
Graphs:
Manually-generated images with constant tones, sharp edges.
11/16/2018
J. Z. Wang & Gio Wiederhold

12. J. Z. Wang & Gio Wiederhold
Type Classification
Photographs:
Images with continuous tones.
11/16/2018
J. Z. Wang & Gio Wiederhold

13. J. Z. Wang & Gio Wiederhold
Photo Classification
Content-based image retrieval
+ statistical classification
11/16/2018
J. Z. Wang & Gio Wiederhold

14. J. Z. Wang & Gio Wiederhold
Experimental Results
Tested on a set of over 10,000 photographic images (i.e., after type classification)
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 & Gio Wiederhold

15. J. Z. Wang & Gio Wiederhold
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 & Gio Wiederhold

16. 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 & Gio Wiederhold

17. False Classifications Objectionable Images
Partially dressed
Undressed area
too small
Dressed but objectionable
Frame and text noise
Dark, low contrast
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J. Z. Wang & Gio Wiederhold

18. 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 & Gio Wiederhold

19. Flow in Website classification
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J. Z. Wang & Gio Wiederhold

20. 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 & Gio Wiederhold

21. Internet High Level Domain Proposal
.... .kids
Sites that are kid-safe, rated by independent organization – several candidates
Supported o.a. by porn industry
Danger: fake .kids sites
.... .xxx
Legitimate sites for adults, easy to filter out for kids
Potential loss of business for porn-industry (work, schools)
No candidate organization – consortium of filter comp's
Fear of government interference and loss of freedom
No mechanism to force objectionable sites into .xxx
Rejected by ICANN, accepted by New.net (Idealab)
11/16/2018
J. Z. Wang & Gio Wiederhold

22. 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?
These results were produced rapidly, they can be improved through further research.
11/16/2018
J. Z. Wang & Gio Wiederhold

23. J. Z. Wang & Gio Wiederhold
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 & Gio Wiederhold