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

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

Image retrieval by hypertext links

This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, the model presented is based on combining evidence of the text content and hypertext structure of the Web. The paper shows that certain types of query are amply served by this form of representation. It also presents a novel means of gathering relevance judgements

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

Watermarking protocol for protecting user\u27s right in content based image retrieval

Content based image retrieval (CBIR) is a technique to search for images relevant to the user&rsquo;s query from an image collection.In last decade, most attention has been paid to improve the retrieval performance. However, there is no significant effort to investigate the security concerning in CBIR. Under the query by example (QBE) paradigm, the user supplies an image as a query and the system returns a set of retrieved results. If the query image includes user&rsquo;s private information, an untrusted server provider of CBIR may distribute it illegally, which leads to the user&rsquo;s right problem. In this paper, we propose an interactive watermarking protocol to address this problem. A watermark is inserted into the query image by the user in encrypted domain without knowing the exact content. The server provider of CBIR will get the watermarked query image and uses it to perform image retrieval. In case where the user finds an unauthorized copy, a watermark in the unauthorized copy will be used as evidence to prove that the user&rsquo;s legal right is infringed by the server provider.<br /

FILTERING GAMBAR DAN VIDEO PORNO PADA JARINGAN

As a source of information, the internet contributes greatly to modern society. Unfortunately, not all information it contains is suitable for everyone. Take for instance, materials containing information about war, violence, drugs, and pornography, themes which are not very suitable for children. Various filtering techniques are used to separate these unsuitable information. Among these techniques, are text based filtering and domain based filtering. Text based filtering works by searching for inappropriate words and calculating its weight. Domain based filtering uses an extensive list of domains it considers as unfavorable. Both of these approaches have several disadvantages. Text based filtering is considered least effective because most harmful contents are image or video. Domain based filtering requires huge resources to check every possible domain name for harmful content. Furthermore, domain names and its contents are frequently updated, domains that were once considered neutral may become harmful anytime in the future. This research uses pornography image detection technique. With this approach, filtering is applied on various data that pass through the network. A computer that stands between the user and internet is used to capture and process this data. Using this technique, we are able to detect about 60% of pornography image positively. It's false positive rate reaches to about 10%. The detection process used in this paper requires a high amount of computation resources. An image that consist of 200.000 pixels takes more than 3 seconds to process. An extensive research at performance area will be able to overcome this limitation

A painterly approach to human skin

technical reportRendering convincing human figures is one of the unsolved goals of computer graphics. Previous work has concentrated on modeling physics of human skin. We have taken a different approach. We are exploring techniques used by artists, specifically artists who paint air-brushed portraits. Our goal is to give the impression of skin without extraneous physical details such as pores, veins, and blemishes. In this paper, we provide rendering algorithms which are easy to incorporate into existing shaders, making rendering skin for medical illustration, computer animations, and other applications fast and simple. We accomplish this by using algorithms for real time drawing and shading of silhouette curves. We also build upon current non-photorealistic lighting methods using complementary colors to convey 3D shape information. Users select areas from a scanned art work and manipulate these areas to create shading models. The flexibility of this method of generating a shading model allows users to portray individuals with different skin tones or to capture the look and feel of a work of art

Averting Robot Eyes

Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms. We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology