Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/112909
DC Field | Value | Language |
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dc.contributor | Department of Industrial and Systems Engineering | - |
dc.creator | Zhu, D | en_US |
dc.creator | Shan, X | en_US |
dc.creator | Wu, C | en_US |
dc.creator | Yung, K | en_US |
dc.creator | Ip, AWH | en_US |
dc.date.accessioned | 2025-05-15T06:58:55Z | - |
dc.date.available | 2025-05-15T06:58:55Z | - |
dc.identifier.issn | 1552-6283 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/112909 | - |
dc.language.iso | en | en_US |
dc.publisher | IGI Global | en_US |
dc.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. | en_US |
dc.rights | 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. | en_US |
dc.subject | Computer Vision | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Obscene Video Detection | en_US |
dc.subject | Pornography classification | en_US |
dc.subject | Self Attention Mechanism | en_US |
dc.subject | Video Analysis | en_US |
dc.subject | Video Classification | en_US |
dc.subject | Vision Transformer | en_US |
dc.subject | ViT-based Models | en_US |
dc.title | Multi frame obscene video detection with vit : an effective for detecting inappropriate content | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | en_US |
dc.identifier.epage | 18 | en_US |
dc.identifier.volume | 20 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.4018/IJSWIS.359768 | en_US |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | International journal on semantic web and information systems, Jan.-Dec. 2024, v. 20, no. 1, p. 1-18 | en_US |
dcterms.isPartOf | International journal on semantic web and information systems | en_US |
dcterms.issued | 2024-12 | - |
dc.identifier.scopus | 2-s2.0-85210775042 | - |
dc.identifier.eissn | 1552-6291 | en_US |
dc.description.validate | 202505 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Guangdong Provincial Demonstrative Industrial College for Artificial Intelligence and Robotics Education | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Zhu_Multi_Frame_Obscene.pdf | 1.28 MB | Adobe PDF | View/Open |
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