Skin Paths for Contextual Flagging Adult Videos

Lecture Notes in Electrical Engineering, 2010

A method for adult content classification and nudity detection is presented. Objective of this method is to classify images into different classes, varying on the degree of adult content. We utilize MPEG-7 descriptors to represent visual information. Skin regions are detected to model adult content more precisely, as well as to eliminate false-positives. Proposed method is tested with conventional image sets. Experimental results indicate that the algorithm has an acceptable detection performance.

Proceedings of the First International Conference on Computer Vision Theory and Applications, 2006

As more people start using the Internet and more content is placed online, the chances that individuals will encounter inappropriate or unwanted adult-oriented content increases. This paper presents a practical and scalable method to efficiently detect many adult-content images, specifically pornographic images. We currently use this system in a search engine that covers a large fraction of the images on the WWW. For each image, face detection is applied and a number of summary features are computed; the results are then fed to a support vector machine for classification. The results show that a significant fraction of adult-content images can be detected.

While the abundance of visual content available on the Internet, and the easy access to such content by all users allows us to find relevant content quickly, it also poses challenges. For example, if a parent wants to restrict the visual content which their child can see, this content needs to either be automatically tagged as offensive or not, or a computer vision algorithm needs to be trained to detect offensive content. One type of potentially offensive content is sexually explicit or provocative imagery. An image may be sexually provocative if it portrays nudity, but the sexual innuendo could also be contained in the body posture or facial expression of the human subject shown in the photo. Existing methods simply analyze skin exposure, but fail to capture the hidden intent behind images. Thus, they are unable to capture several important ways in which an image might be sexually provocative, hence offensive to children. We propose to address this problem by extracting a unified feature descriptor constituting the percentage of skin exposure , the body posture of the human in the image, and his/her gestures and facial expressions. We learn to predict these cues, then train a hierarchical model which combines them. We show in experiments that this model more accurately detects sexual innuendos behind images.

International Journal of Advanced Computer Science and Applications, 2021

With the increasing amount of pornography being uploaded on the Internet today, arises the need to detect and block such pornographic websites, especially in Eastern cultural countries. Studying pornographic images and videos, show that explicit sensitive objects are typically one of the main charac-teristics portraying the unique aspect of pornography content. This paper proposed a classification method on pornographic visual content, which involved detecting sensitive objects using object detection algorithms. Initially, an object detection model is used to identify sensitive objects on visual content. The detection results are then used as high-level features combined with two other high-level features including skin body and human presence information. These high-level features finally are fed into a fusion Support Vector Machine (SVM) model, thus draw the eventual decision. Based on 800 videos from the NDPI-800 dataset and the 50.000 manually collected images, the evaluation re...

2009 IEEE International Workshop on Multimedia Signal Processing, 2009

By analyzing the low level features of images only, skin detection in visual data cannot be solved. To compensate for this major drawback of many approaches, we combine a state of the art recognition algorithm with color model based skin detection. Detected faces in videos are the basis for adaptive skin-color models, which are propagated throughout the video, providing a more precise and accurate model in its recognition performance than pure color based approaches. The approach is able to run in real-time and does not need prior dataspecific training. We received challenging online videos from an online service provider and use additional videos from public web platforms covering a grand variety of different skin-colors, illumination circumstances, image quality and difficulty levels.

Proceeding of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams - AREA '08, 2008

We propose a straightforward skin detection method for online videos. To overcome varying illumination circumstances and a variety of skin colors, we introduce a multiple model approach which can be carried out independently per model. The color models are initiated by skin detection based on face detection and adapted in real time. Our approach outperforms static approaches both in precision and runtime. If we detect a face in a scene, the number of false positives can be diminished significantly. Evaluation is carried out on publicly available on-line videos showing that adaptive multiple model outperforms static methods in classification precision and suppression of false positives.

2013

This paper presents a novel approach for video adult detection using face shape, skin threshold technique and neural network. The goal of employing skin-color information is to select the appropriate color model that allows verifying pixels under different lighting conditions and other variations. Then, the output videos are classified by neural network. The simulation shows that this system achieved 95.4% of the true rate.

Nowadays, the Internet is one of the most important things in a human's life. The unlimited access to information has the potential for people to gather any data related to their needs. However, this sophisticated technology also bears a bad side, for instance negative content information. Negative content can come in the form of images that contain pornography. This paper presents the development of a skin classification scheme as part of a negative content filtering system. The data are trained by grey-level co-occurrence matrices (GLCM) texture features and then used to classify skin color by support vector machine (SVM). The tests on skin classification in the skin and non-skin categories achieved an accuracy of 100% and 97.03%, respectively. These results indicate that the proposed scheme has potential to be implemented as part of a negative content filtering system.

This paper presents an algorithm for detecting nudity in color images. A skin color distribution model based on the RGB, Normalized RGB, and HSV color spaces is constructed using correlation and linear regression. The skin color model is used to identify and locate skin regions in an image. These regions are analyzed for clues indicating nudity or non-nudity such as their sizes and relative distances from each other. Based on these clues and the percentage of skin in the image, an image is classified nude or non-nude. The skin color distribution model performs with 96.29% recall and 6.76% false positive rate on a test set consisting of 2,303,824 manually labeled skin pixels and 24,285,952 manually labeled non-skin pixels. The Nudity Detection Algorithm is able to detect nudity with a 94.77% recall and a false positive rate of 5.04% on a set of images consisting of 421 nude images and 635 non-nude images.

International Journal of Intelligent Engineering and Systems, 2022

This paper introduces a new dataset named Large-Scale Pornographic Dataset for detection and classification (LSPD) that intends to advance the standard quality of pornographic visual content classification and sexual object detection tasks. As we recognize, the LSPD dataset is the first ever dataset for both object detection and image/video classification tasks in this area. The dataset gathers a large-scale corpus of pornographic/nonpornographic images and videos containing a rich diversity of context. The images and videos are not only labelled with their representative class but are also annotated by polygon masks of four private sexual objects (breasts, male and female genitals, and anuses). Our dataset contains 500,000 images and 4,000 videos, with more than 50,000 annotated images. To ensure fair use of the dataset, we present a detailed statistical analysis and provide baseline benchmarking scenarios for both image/video classification and instance detection/segmentation tasks. Finally, we evaluate the performance of four object detection algorithms and a Convolutional Neural Network (CNN) classifier on these scenarios.