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

With rapid development of the Internet, web contents become huge. Most of the websites are publicly available, and anyone can access the contents from anywhere such as workplace, home and even schools. Nevertheless, not all the web contents are appropriate for all users, especially children. An example of these contents is pornography images which should be restricted to certain age group. Besides, these images are not safe for work (NSFW) in which employees should not be seen accessing such contents during work. Recently, convolutional neural networks have been successfully applied to many computer vision problems. Inspired by these successes, we propose a mixture of convolutional neural networks for adult content recognition. Unlike other works, our method is formulated on a weighted sum of multiple deep neural network models. The weights of each CNN models are expressed as a linear regression problem learned using Ordinary Least Squares (OLS). Experimental results demonstrate that the proposed model outperforms both single CNN model and the average sum of CNN models in adult content recognition.Comment: To be published in LNEE, Code: github.com/mundher/NSF

Efficient filtering of adult content using textual information

Nowadays adult content represents a non negligible proportion of the Web content. It is of the utmost importance to protect children from this content. Search engines, as an entry point for Web navigation are ideally placed to deal with this issue. In this paper, we propose a method that builds a safe index i.e. adult-content free for search engines. This method is based on a filter that uses only textual information from the web page and the associated URL

Link Graph Analysis for Adult Images Classification

In order to protect an image search engine's users from undesirable results adult images' classifier should be built. The information about links from websites to images is employed to create such a classifier. These links are represented as a bipartite website-image graph. Each vertex is equipped with scores of adultness and decentness. The scores for image vertexes are initialized with zero, those for website vertexes are initialized according to a text-based website classifier. An iterative algorithm that propagates scores within a website-image graph is described. The scores obtained are used to classify images by choosing an appropriate threshold. The experiments on Internet-scale data have shown that the algorithm under consideration increases classification recall by 17% in comparison with a simple algorithm which classifies an image as adult if it is connected with at least one adult site (at the same precision level).Comment: 7 pages. Young Scientists Conference, 4th Russian Summer School in Information Retrieva

Automated Discovery of Internet Censorship by Web Crawling

Censorship of the Internet is widespread around the world. As access to the web becomes increasingly ubiquitous, filtering of this resource becomes more pervasive. Transparency about specific content that citizens are denied access to is atypical. To counter this, numerous techniques for maintaining URL filter lists have been proposed by various individuals and organisations that aim to empirical data on censorship for benefit of the public and wider censorship research community. We present a new approach for discovering filtered domains in different countries. This method is fully automated and requires no human interaction. The system uses web crawling techniques to traverse between filtered sites and implements a robust method for determining if a domain is filtered. We demonstrate the effectiveness of the approach by running experiments to search for filtered content in four different censorship regimes. Our results show that we perform better than the current state of the art and have built domain filter lists an order of magnitude larger than the most widely available public lists as of Jan 2018. Further, we build a dataset mapping the interlinking nature of blocked content between domains and exhibit the tightly networked nature of censored web resources

Internet Filters: A Public Policy Report (Second edition; fully revised and updated)

No sooner was the Internet upon us than anxiety arose over the ease of accessing pornography and other controversial content. In response, entrepreneurs soon developed filtering products. By the end of the decade, a new industry had emerged to create and market Internet filters....Yet filters were highly imprecise from the beginning. The sheer size of the Internet meant that identifying potentially offensive content had to be done mechanically, by matching "key" words and phrases; hence, the blocking of Web sites for "Middlesex County," or words such as "magna cum laude". Internet filters are crude and error-prone because they categorize expression without regard to its context, meaning, and value. Yet these sweeping censorship tools are now widely used in companies, homes, schools, and libraries. Internet filters remain a pressing public policy issue to all those concerned about free expression, education, culture, and democracy. This fully revised and updated report surveys tests and studies of Internet filtering products from the mid-1990s through 2006. It provides an essential resource for the ongoing debate