Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/112909
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Title: Multi frame obscene video detection with vit : an effective for detecting inappropriate content
Authors: Zhu, D
Shan, X
Wu, C
Yung, K 
Ip, AWH
Issue Date: Dec-2024
Source: International journal on semantic web and information systems, Jan.-Dec. 2024, v. 20, no. 1, p. 1-18
Abstract: With the development of the Internet, people are surrounded by various types of information daily, including obscene videos. The quantity of such videos is increasing daily, making the detection and filtering of this information a crucial step in preventing its spread. However, a significant challenge remains in detecting obscene information in obscure scenarios, like indecent behavior occurring while wearing normal clothing, causing significant negative impacts, such as harmful influence on children. To address this issue, an innovative multi frame obscene video detection base on ViT is proposed by this manuscript per the authors, aiming to automatically detect and filter obscene content in videos. Extensive experiments conducted on the public NPDI dataset demonstrate that this method achieves better results than existing state-of-the-art methods, achieving 96.2%. Additionally, it achieves satisfactory classification accuracy on a dataset of obscure obscene videos.This provides a powerful tool for future video censorship and protects minors and the general public.
Keywords: Computer Vision
Deep Learning
Obscene Video Detection
Pornography classification
Self Attention Mechanism
Video Analysis
Video Classification
Vision Transformer
ViT-based Models
Publisher: IGI Global
Journal: International journal on semantic web and information systems 
ISSN: 1552-6283
EISSN: 1552-6291
DOI: 10.4018/IJSWIS.359768
Rights: This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.
The following publication Zhu, D., Shan, X., Wu, C., Yung, K., & Ip, A. W. (2024). Multi Frame Obscene Video Detection With ViT: An Effective for Detecting Inappropriate Content. International Journal on Semantic Web and Information Systems (IJSWIS), 20(1), 1-18 is available at https://dx.doi.org/10.4018/IJSWIS.359768.
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