Detecting Pornographic Images by Localizing Skin ROIs

Introduction

With the galloping evolution of the Internet during the last two decades, pornographic images can be readily accessed even by certain sensitive groups of people, such as adolescents. Moreover, the ability to distribute and share images through public HTTP services without an autonomous content supervision process intervening in the content sharing loop encourages circulation of illicit images. For instance, pornographic content distributors may often exploit unprotected Web services in order to circulate or exchange child pornography and general pornographic content. The impact of pornography to people has long become a rising issue of concern and was under the spotlight of psychologists decades ago. Psychological research dated back to the 80's stressed out that the exposure of children to pornography effectively impedes the smoothness of their behavioral evolution. In the same direction, similar studies have indicated that intense exposition to pornographic material affects human behavior and mood in adults. For some representative papers dealing with this problem see Brown, Amoroso, and Ware (1976), Pierce (1984), Cook, Fosen, and Pacht (1971), Lo and Wei (2005), Meyer (1972), and Padgett, Brislin-Slütz, and Neal (1989). The premier source of pornographic information on the Web has traditionally been pornographic images and video. We assert that the semantic load of such images is essentially the primal carrier of pornographic information to effectively engage the focus of attention of the end user, e.g., when browsing pornographic web pages. Thus, we contend that pornographic image detection is a crucial aspect in the loop of effectively identifying pornographic web pages.

A system tailored towards identifying pornographic images should exhibit a robust capability in distinguishing between regular benign images and assorted pornographic images. In a real world content filtering scenario, zero-error categorization is still an unrealized goal. That being said, systems proposed in the literature are characterized by certain strengths and weaknesses in detecting pornographic content. Among many challenging problems in this computer vision problem, the difficulty in constructing an accurate algorithmic framework for detecting pornography is often attributed to varied photometric conditions (which often come in the form of bad illumination), unconstrained clutter, occlusions and variation in the poses of involved human subjects. Thus, it is intrinsically difficult to construct a precise algorithm that encodes accurate prior information about what a pornographic image really is. To the best of our knowledge, many content filtering systems operate satisfactorily in identifying pornographic web pages by means of pornographic image detection. Despite the abundant availability of textual and structural information in common pornographic web pages, a system can exhibit more robust accuracy by being able to tell pornographic and benign images apart. Later, we review some previously proposed systems in the literature aimed at pornographic web page detection.