Pornographic Image Recognition via Weighted Multiple Instance Learning

In the era of Internet, recognizing pornographic images is of great significance for protecting children's physical and mental health. However, this task is very challenging as the key pornographic contents (e.g., breast and private part) in an image often lie in local regions of small size. In this paper, we model each image as a bag of regions, and follow a multiple instance learning (MIL) approach to train a generic region-based recognition model. Specifically, we take into account the region's degree of pornography, and make three main contributions. First, we show that based on very few annotations of the key pornographic contents in a training image, we can generate a bag of properly sized regions, among which the potential positive regions usually contain useful contexts that can aid recognition. Second, we present a simple quantitative measure of a region's degree of pornography, which can be used to weigh the importance of different regions in a positive image. Third, we formulate the recognition task as a weighted MIL problem under the convolutional neural network framework, with a bag probability function introduced to combine the importance of different regions. Experiments on our newly collected large scale dataset demonstrate the effectiveness of the proposed method, achieving an accuracy with 97.52% true positive rate at 1% false positive rate, tested on 100K pornographic images and 100K normal images.Comment: 9 pages, 3 figure

A Novel Scheme for Intelligent Recognition of Pornographic Images

Harmful contents are rising in internet day by day and this motivates the essence of more research in fast and reliable obscene and immoral material filtering. Pornographic image recognition is an important component in each filtering system. In this paper, a new approach for detecting pornographic images is introduced. In this approach, two new features are suggested. These two features in combination with other simple traditional features provide decent difference between porn and non-porn images. In addition, we applied fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron) and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of system was evaluated over 18354 download images from internet. The attained precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on test dataset. Achieved results verify the performance of proposed system versus other related works

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

Protecting the Least of These: A New Approach to Child Pornography Pandering Provisions

The pandering of child pornography - selling, distributing, or conveying the impression that one possesses sexually graphic images of children for sale or distribution - facilitates actual harm to children, such as molestation. Yet legislative attempts to curb pandering inevitably implicate concerns about panderers\u27 First Amendment rights. This Note argues that in balancing the vulnerability of children against the power of the First Amendment, the law must shift to focus more on the subject of this grievous harm - children. This approach will appropriately extend protection to a subset of the population that is least able to protect itself

Pornography Debate, Gaze and Spectatorship in Sarah Daniels’s Masterpieces

Masterpieces by Sarah Daniels has been described as a voice in the debate on pornography, expressing the anti-pornography position as opposed to the liberal feminist stance in this debate. Despite its ideological clarity reported by many reviewers and critics, the play has been commented upon as deficient or inadequate because of evoking conflicting interpretations and ambiguity. The paper argues that these deficiencies stem from the play’s concern with the distribution of agency and passivity along gender lines as well as the influence of generic and essentialist notions of genders on the perception of social and individual power relations particularly in the domain of eroticism and sexuality. One of the key issues of the play is the question to what extent and in what ways human perception is conditioned by the place of the subject in relation to the agency/passivity dichotomy and his or her viewing/reading position in relation to erotic and pornographic material