Citation

Momo, Mhd Adel and Abdul Karim, Hezerul and Sy, Michael Aaron G. and Albunni, Ahmad and Tan, Myles Joshua Toledo and AlDahoul, Nouar (2023) Evaluation of Convolution and Attention Networks for Nudity and Pornography Detection in Sketch Images. In: 2023 IEEE Symposium on Computers & Informatics (ISCI), 14-15 October 2023, Shah Alam, Malaysia.

Text 14.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

— Internet has become a main source of information for people in numerous areas. However, internet can be useful or harmful according to types of contents exposed. When the visual sexual and violent contents are available, the danger targets children and youths and causes a damage in their mental health. Consequently, content moderators are required to be employed in all websites and social media platforms. Several companies involve human to review, filter, and edit visual sexual and violent contents. However, manual reviewing and filtering is costly and tough. Therefore, big companies such as Amazon and Microsoft used machine learning based solutions to automate the content moderation process. This paper proposes a new solution for content moderation that targets sketch images specifically. Three state of the art models including EfficientNetB7 convolutional neural network (CNN), vision transformer, and convolution and attention (CoAtNet) have been employed for this task to detect nudity and pornography in sketch images. A novel benchmarking dataset including 4417 sketch images were used for training and testing. It was found that our proposed methods outperformed the cloud-based content moderation solutions such as AWS, and Microsoft Azure in terms of accuracy (97%) and F1-score (97%).

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Pornography Detection, Mental health
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	21 Feb 2024 05:50
Last Modified:	21 Feb 2024 05:50
URII:	http://shdl.mmu.edu.my/id/eprint/12093

Downloads

Edit (login required)