Abstract
We present a method to detect automatically pornographic content on the Web. Our method combines techniques from language engineering and image analysis within a machine-learning framework. Experimental results show that it achieves nearly perfect performance on a set of hard cases.
See http://websearch.about.com/internet/websearch/library/myths for related statistics.
Consult [4] for an evaluation of third-party and self-regulating rating schemes.
An evaluation copy of a commercial product included a ~12,000 strong URL blacklist which contained only 36 out of the 500 pornographic URLs we easily summoned in a day.
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Chandrinos, K.V., Androutsopoulos, I., Paliouras, G., Spyropoulos, C.D. (2000). Automatic Web Rating: Filtering Obscene Content on the Web. In: Borbinha, J., Baker, T. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2000. Lecture Notes in Computer Science, vol 1923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45268-0_50
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DOI: https://doi.org/10.1007/3-540-45268-0_50
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