Combating Cyberbullied Images in the Real World: A Comparative Study of CNN Architectures

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

Image-based cyberbullying brings more complexity compared to text-based forms, presenting new challenges that tra-ditional methods struggle to address. Owing to the rapid advancement of deep learning, computer vision technology has become a powerful tool to address this issue. In recent years, Convolutional Neural Networks (CNNs) have gained prominence for image processing and recognition because of their excellent feature extraction abilities. Initially, this paper conducts a comprehensive review of the literature in the fields of computer vision and cyberbullying detection and provides an extensive comparison of the performance among different CNN architectures, including VGG, ResNet, and WRNs, on a dataset of cyberbullying images. It shows and discusses that the VGG architecture outperforms other architectures in terms of performance and computational efficiency.

Identifier

85213336279 (Scopus)

ISBN

[9798350365221]

Publication Title

ICNSC 2024 - 21st International Conference on Networking, Sensing and Control: Artificial Intelligence for the Next Industrial Revolution

External Full Text Location

https://doi.org/10.1109/ICNSC62968.2024.10759997

Grant

62302223

Fund Ref

Eastern Kentucky University

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