Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/85818| Title: | Private body part detection using deep learning |
| Authors: | Tabone, André Bonnici, Alexandra Cristina, Stefania Farrugia, Reuben A. Camilleri, Kenneth P. |
| Keywords: | Computational intelligence Computational complexity Artificial intelligence Big data Data mining Deep learning (Machine learning) Pornography |
| Issue Date: | 2020 |
| Publisher: | ICPRAM |
| Citation: | Tabone, A., Bonnici, A., Cristina, S., Farrugia, R. A., & Camilleri, K. P. (2020). Private body part detection using deep learning. 9th International Conference on Pattern Recognition Applications and Methods. 205-211. |
| Abstract: | Fast and accurate detection of sexually exploitative imagery is necessary for law enforcement agencies to allow for prosecution of suspect individuals. In literature, techniques which can be used to assist law enforcement agencies only determine whether the image content is pornographic or benign. In this paper, we provide a review on classical handcrafted-feature based and deep-learning based pornographic detection in images and describe a framework which goes beyond this, to identify the location of genitalia in the image. Despite this being a computationally complex task, we show that by learning multiple features, a MobileNet framework can achieve an accuracy of 76.29% in the correct labelling of female and male sexual organs. |
| URI: | https://www.um.edu.mt/library/oar/handle/123456789/85818 |
| Appears in Collections: | Scholarly Works - FacICTCCE |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 91015.pdf Restricted Access | 176.82 kB | Adobe PDF | View/Open Request a copy |
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