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All Journal International Journal of Electrical and Computer Engineering
Mridula Batra
Manav Rachna International Institute Of Research And Studies
Computer Science & IT Electrical & Electronics Engineering

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Automatic video censoring system using deep learning Yash Verma; Madhulika Bhatia; Poonam Tanwar; Shaveta Bhatia; Mridula Batra
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6744-6755

Abstract

Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications.
Co-Authors Madhulika Bhatia Poonam Tanwar Shaveta Bhatia Yash Verma