A Deep Learning Filtration Framework to Eliminate Not Safe For Work content in Digital media

Prasanna Kumar, Rohit (2024) A Deep Learning Filtration Framework to Eliminate Not Safe For Work content in Digital media. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (626kB) | Preview

Abstract

Not Suitable for Work (NSFW) is a term which is used on the internet to warn users about content with inappropriate material such as nudity which might not be suitable to access at any work or study environment.

Most websites and chat applications have functionalities of NSFW content filtration which can disable the uploading of images or texts containing explicit words. However, this technique is not error free as there are cases where the filter blocks even the safe content. Besides that, there is no process currently in action which can filter for video content. The challenge here would be implementation of NSFW filter in a video content to identify NSFW content hidden within a video, which normally cannot be deduced by filename or the video thumbnail. This research proposes a Deep Learning Filtration Framework to filter the NSFW content in a Video. The proposed framework combines a Detection Model and Filtration technique to extract the content out of video. The Deep Learning Model is implemented using YOLO (You Only Look Once) trained on NudeNet classifier dataset for training and LSPD Dataset consisting of 500,000 images and 4,000 videos, with 93,810 labelled instances in 50,212 images for testing. The model is evaluated based on accuracy of the prediction. Results demonstrate values for safe and unsafe image where in values more than 75 percent in confidence are considered NSFW.

This research is on interest to provide a safe environment in the social media and chat sites to help avoid accidental exposure to such content and help the social websites/applications sustain their main purpose of social interaction based on demographics and not allow unmoderated content to be distributed at ease.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Stynes, Paul UNSPECIFIED
Uncontrolled Keywords:	NSFW; YOLO; CNN
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence > Computer vision Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence > Computer vision Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites
Divisions:	School of Computing > Master of Science in Artificial Intelligence
Depositing User:	Ciara O'Brien
Date Deposited:	20 Jun 2025 09:50
Last Modified:	20 Jun 2025 09:50
URI:	https://norma.ncirl.ie/id/eprint/7961

Actions (login required)

View Item