Detecting Cyberbullying in Images Using Deep Learning: A VGG19-Based Approach

Title: Detecting Cyberbullying in Images Using Deep Learning: A VGG19-Based Approach Abstract: Cyberbullying has become a widespread social issue that impacts internet users, notably through the exploitation of visual images. In past studies, the detection of cyberbullying in images was explored but faced difficulties such as limited accuracy and reliance on multimodal techniques. We address this gap by proposing an improved method for identifying cyberbullying in images using a deep learning model. Specifically, we used the VGG19 architecture to evaluate a real-world dataset of 19,300 images related to cyberbullying, achieving superior performance compared to existing methods. Our analysis identifies critical contextual characteristics in cyberbullying images that distinguish them from standard offensive image material, such as violence or nudity. We show that VGG19 outperforms the multimodal classification model proposed in previous research, with a mean detection accuracy of 95%. These findings demonstrate the utility of convolutional neural networks (CNNs) in solving the particular issues given by contextual images of cyberbullying. Our research contributes to the development of improved techniques for combating cyberbullying in visual media. Virtual: https://events.vtools.ieee.org/m/480464