Fine-Grained Pornographic Image Recognition with Multi-Instance Learning

Image has become an essential medium for expressing meaning and disseminating information.Many images are uploaded to the Internet,among which some are pornographic,causing adverse effects on public psychological health.To create a clean and positive Internet environment,network enforcement agencies need an automatic and efficient pornographic image recognition tool.Previous studies on pornographic images mainly rely on convolutional neural networks(CNN).Because of CNN’s many parameters,they must rely on a large labeled training dataset,which takes work to build.To reduce the effect of the database on the recognition performance of pornographic images,many researchers view pornographic image recognition as a binary classification task.In actual application,when faced with pornographic images of various features,the performance and recognition accuracy of the network model often decrease.In addition,the pornographic content in images usually lies in several small-sized local regions,which are not a large proportion of the image.CNN,this kind of strong supervised learning method,usually cannot automatically focus on the pornographic area of the image,thus affecting the recognition accuracy of pornographic images.This paper established an image dataset with seven classes by crawling pornographic websites and Baidu Image Library.A weakly supervised pornographic image recognition method based on multiple instance learning(MIL)is proposed.The Squeeze and Extraction(SE)module is introduced in the feature extraction to strengthen the critical information and weaken the influence of non-key and useless information on the result of pornographic image recognition.To meet the requirements of the pooling layer operation in Multiple Instance Learning,we introduced the idea of an attention mechanism to weight and average instances.The experimental results show that the proposed method has better accuracy and F1 scores than other methods.

Learning Association Rules and Tracking the Changing Concepts on Webpages:An Effective Pornographic Websites Filtering Approach

We applied the decision tree algorithm to learn association rules between webpage’s category(pornographic or normal) and the critical features.Based on these rules, we proposed an efficient method of filtering pornographic webpages with the following major advantages: 1) a weighted window-based technique was proposed to estimate for the condition of concept drift for the keywords found recently in pornographic webpages; 2) checking only contexts of webpages without scanning pictures; 3) an incremental learning mechanism was designed to incrementally update the pornographic keyword database.

Transfer Learning on Deep Neural Networks to Detect Pornography

While the internet has a lot of positive impact on society,there are negative components.Accessible to everyone through online platforms,pornography is,inducing psychological and health related issues among people of all ages.While a difficult task,detecting pornography can be the important step in determining the porn and adult content in a video.In this paper,an architecture is proposed which yielded high scores for both training and testing.This dataset was produced from 190 videos,yielding more than 19 h of videos.The main sources for the content were from YouTube,movies,torrent,and websites that hosts both pornographic and non-pornographic contents.The videos were from different ethnicities and skin color which ensures the models can detect any kind of video.A VGG16,Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49,0.49 and 0.78 respectively.Finally,utilizing transfer learning,a convolutional neural network was designed and yielded an accuracy of 0.98.