Name
Abstract | ||
|---|---|---|
The pornographic materials including videos and images are easily in reach for everyone, including under-age youths, allover Internet. It is also an aim for popular social network applications to contain no public pornographic materials. However, their frequent existence throughout all the Internet and huge amount of available images and videos there, make it impossible for manual monitoring to discriminate positive items (porn image or video) from benign images (non-porn image or video). Therefore, automatic detection techniques can be very useful here. But, the traditional machine learning models face many challenges. For example, they need to tune their many parameters, to select the suitable feature set, to select a suitable model. Therefore, this paper proposes an intelligent filtering system model based on a recent convolutional neural networks where it bypasses the aforementioned challenges. We show that the proposed model outperforms the recent machine learning based models. It also outperforms the state of the art deep learning based models. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11063-020-10238-3 | NEURAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Content filtering,Pornographic material recognition,Deep learning,Convolutional neural networks | Journal | 52.0 |
Issue | ISSN | Citations |
SP1.0 | 1370-4621 | 1 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Xu | 1 | 1 | 0.36 |
| Hamid Parvin | 2 | 263 | 41.94 |
| Hadi Izadparast | 3 | 1 | 0.36 |