787 research outputs found
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SafeVchat: Detecting Obscene Content and Misbehaving Users in Online Video Chat Services ; CU-CS-1077-11
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
A survey on Datamining in Cyber Bullying
No Abstrac
Designing Light Filters to Detect Skin Using a Low-powered Sensor
Detection of nudity in photos and videos, especially prior to uploading to the internet, is vital to solving many problems related to adolescent sexting, the distribution of child pornography, and cyber-bullying. The problem with using nudity detection algorithms as a means to combat these problems is that: 1) it implies that a digitized nude photo of a minor already exists (i.e., child pornography), and 2) there are real ethical and legal concerns around the distribution and processing of child pornography. Once a camera captures an image, that image is no longer secure. Therefore, we need to develop new privacy-preserving solutions that prevent the digital capture of nude imagery of minors. My research takes a first step in trying to accomplish this long-term goal: In this thesis, I examine the feasibility of using a low-powered sensor to detect skin dominance (defined as an image comprised of 50% or more of human skin tone) in a visual scene. By designing four custom light filters to enhance the digital information extracted from 300 scenes captured with the sensor (without digitizing high-fidelity visual features), I was able to accurately detect a skin dominant scene with 83.7% accuracy, 83% precision, and 85% recall. The long-term goal to be achieved in the future is to design a low-powered vision sensor that can be mounted on a digital camera lens on a teen\u27s mobile device to detect and/or prevent the capture of nude imagery. Thus, I discuss the limitations of this work toward this larger goal, as well as future research directions
Development of automatic obscene images filtering using deep learning
Because of Internet availability in most societies, access to pornography has be-come a severe issue. On the other side, the pornography industry has grown steadily, and its websites are becoming increasingly popular by offering potential users free passes. Filtering obscene images and video frames is essential in the big data era, where all kinds of information are available for everyone. This paper proposes a fully automated method to filter any storage device from obscene vid-eos and images using deep learning algorithms. The whole recognition process can be divided into two stages, including fine detection and focus detection. The fine detection includes skin color detection with YCbCr and HSV color spaces and accurate face detection using the Adaboost algorithm with Haar-like features. Moreover, focus detection uses AlexNet transfer learning to identify the obscene images which passed stage one. Results showed the effectiveness of our pro-posed algorithm in filtering obscene images or videos. The testing accuracy achieved is 95.26% when tested with 3969 testing images
Deep Architectures for Content Moderation and Movie Content Rating
Rating a video based on its content is an important step for classifying
video age categories. Movie content rating and TV show rating are the two most
common rating systems established by professional committees. However, manually
reviewing and evaluating scene/film content by a committee is a tedious work
and it becomes increasingly difficult with the ever-growing amount of online
video content. As such, a desirable solution is to use computer vision based
video content analysis techniques to automate the evaluation process. In this
paper, related works are summarized for action recognition, multi-modal
learning, movie genre classification, and sensitive content detection in the
context of content moderation and movie content rating. The project page is
available at https://github.com/fcakyon/content-moderation-deep-learning
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