WebGuard: a Web filtering engine combining textual, structural, and visual content-based analysis

Hammami, Mohamed; Chahir, Youssef; Chen, L.

doi:10.1109/tkde.2006.34

Cited by 98 publications

(49 citation statements)

References 15 publications

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“…The classification system of SafeSurf is more detailed. In order to describe harmfulness of web contents to each age group, it uses eleven categories [2], [8], [5], [10].…”

Section: Pics (Platform For Internet Content Selection)mentioning

confidence: 99%

Harmful Contents Classification Using the Harmful Word Filtering and SVM

Lee

Chung

et al. 2007

Computational Science – ICCS 2007

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Abstract. As World Wide Web is more popularized nowadays, it is also creating many problems due to uncontrolled flood of information. The pornographic, violent and other harmful information freely available to the youth, who must be protected by the society, or other users who lack the power of judgment or self-control is creating serious social problems. To resolve those harmful words, various methods proposed and studied. This paper proposes and implements the protecting system that protects internet youth user from harmful contents. To effectively classify harmful/harmless contents, this system uses two steps of classification: harmful word filtering and SVM learning based filtering. We achieved result that the average precision of 92.1%.

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“…The classification system of SafeSurf is more detailed. In order to describe harmfulness of web contents to each age group, it uses eleven categories [2], [8], [5], [10].…”

Section: Pics (Platform For Internet Content Selection)mentioning

confidence: 99%

Harmful Contents Classification Using the Harmful Word Filtering and SVM

Lee

Chung

et al. 2007

Computational Science – ICCS 2007

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“…At least, three categories of intelligent content Web filtering can be distinguished : (i) textual content Web filtering [1], (ii) structural content Web filtering [2], [3] and (iii) Visual content Web filtering [4]. Other Web filtering solutions are based on an analysis of textual, structural and visual contents, of a Web page [5].…”

Section: Web Filtering: Related Workmentioning

confidence: 99%

Combining Classifiers for Web Violent Content Detection and Filtering

Guermazi¹,

Hammami²,

Hamadou³

2007

Computational Science – ICCS 2007

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Abstract. Keeping people away from litigious information becomes one of the most important research area in network information security. Indeed, Web filtering is used to prevent access to undesirable Web pages. In this paper we review some existing solutions, then we propose a violent Web content detection and filtering system called "WebAngels filter" which uses textual and structural analysis. "WebAngels filter" has the advantage of combining several data-mining algorithms for Web site classification. We discuss how the combination learning based methods can improve filtering performances. Our preliminary results show that it can detect and filter violent content effectively.

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“…Zheng은 먼저 피부 색상 모델을 피부 영역을 추출하기 위해 서 적용했고, 추출된 피부 색상 영역에서 획득된 여러 가지 특 징들을 뉴럴 네트워크에 적용하여 유해 영상물을 검출하였다 [4]. Hammami는 피부 색상에 기반한 시각적인 분석을 이용해 성인 영상을 검출하는 Webguard 시스템을 개발하였다 [5]. 웹 페이지 내에 피부 색상 픽셀의 포함 정도를 나타내는 피부 색 상과 관련된 시각적인 특징들을 사용했다.…”

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Adult Image Detection Using Skin Color and Multiple Features

Jang¹,

Choi²,

Kim³

2010

Journal of the Korea Society of Computer and Information

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Extracting skin color is significant in adult image detection. However, conventional methods still have essential problems in extracting skin color. That is, colors of human skins are basically not the same because of individual skin difference or difference races. Moreover, skin regions of images may not have identical color due to makeup, different cameras used, etc. Therefore, most of the existing methods use predefined skin color models. To resolve these problems, in this paper, we propose a new adult image detection method that robustly segments skin areas with an input image-adapted skin color distribution model, and verifies if the segmented skin regions contain naked bodies by fusing several representative features through a neural network scheme. Experimental results show that our method outperforms others through various experiments. We expect that the suggested method will be useful in many applications such as face detection and objectionable image filtering.▸Keyword : 성인 영상(Adul t image), 피부 색상(Ski n color), 다중 특징(Mul tiple features), 뉴 럴 네트워크(Neural network)

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WebGuard: a Web filtering engine combining textual, structural, and visual content-based analysis

Cited by 98 publications

References 15 publications

Harmful Contents Classification Using the Harmful Word Filtering and SVM

Harmful Contents Classification Using the Harmful Word Filtering and SVM

Combining Classifiers for Web Violent Content Detection and Filtering

Adult Image Detection Using Skin Color and Multiple Features

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