Recognition of Pornographic Web Pages by Classifying Texts and Images

Abstract

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

Classification-based Retrieval Methods to Enhance Information Discovery on the Web

“…Also it is possible to navigate into the directory, specializing /generalizing the original query. A global view of the relationships between data mining techniques used in this thesis and the applications generated from the use of these techniques over the query log data is given in Figure 1 [11] and [13] and [17]. Of course, the applications will follow a set of design criteria.…”

Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks

“…This method classifies a region of pixels as either skin or non-skin. The skin color can be detected manually using a color range [1], computed color histograms [4], or parametric color distribution functions [3]. Once a skin color model of the image has been defined, the adult image can be detected by a simple skin color histogram threshold, or by passing the statistics of the skin information to a classifier [11].…”

Scalable misbehavior detection in online video chat services

“…Skin color based detection mechanisms are broadly used and achieve acceptable performance in terms of precision and recall for pornographic content detection [10][11] [12][13] [14]. This approach identifies skin exposure regions in an image using a statistical color model.…”