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

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

Time-Sensitive Adaptive Model for Adult Image Classification

Images play an important role in modern internet communications, but not all of the images shared by the users are appropriate, and it is necessary to check and reject the inappropriate ones. Deep neural networks do this task perfectly, but it may not be necessary to use maximum power for all images. Many easier-to-identify images may be classified at a lower cost than running the full model. Also, the pressure on the system varies from time to time, so an algorithm that can produce the best possible results for different budgets is very useful. For this purpose, a deep convolutional neural network with the ability to generate several outputs from its various layers has been designed. Each output can be considered as a classifier with its own cost and accuracy. A selector is then used to select and combine the results of these outputs to produce the best possible result in the specified time budget. The selector uses a reinforcement learning model, which, despite the time-consuming learning phase, is fast at execution time. Our experiments on challenging social media images dataset show that the proposed model can reduce the processing time by 32 % by sacrificing only 1.4 % of accuracy compared to the VGG-f network. Also, using different metrics such as F1-score and AUC (the Area Under the Curve in the accuracy vs. time budget chart), the superiority of the proposed model at different time budgets over the base model is shown