Pornography Detection with the Wisdom of Crowds

With rapid development of the Internet, much attention has been paid to the problem of children exposed to Internet pornography. Existing detection techniques, which mainly focus on pornography content analysis have gained much success. However, they still meet challenges in practical Web environment due to the great computational costs and the difficulties in dealing with various pornography forms. We attempt to solve this problem from a new perspective with the wisdom of crowds in search engine click-through logs. Inspired by the idea that different pornography Web pages may be oriented by similar search keywords, a label propagation method on click-through bipartite graph is proposed which can locate pornography Web pages from a small set (a few hundreds) of manually labeled seed pages. Experiments performed on datasets collected from both English and Chinese search engines show that the proposed algorithm can identify different forms of Internet pornography both effectively and efficiently.