Overcoming the Challenge of Cyberbullying Detection in Images: A Deep Learning Approach with Image Captioning and OCR Integration

Abstract

Cyberbullying is a serious concern in today's digital age. The rapid increase in the use of social media platforms has made cyberbullying even more prevalent. The form of cyberbullying has also evolved with time. In the era of Web 1.0, cyberbullying was limited to text-based data, but with the advent of Web 2.0 and 3.0, it has expanded to images and multi-modal data. Detecting cyberbullying in text-based data is relatively easy as various natural language processing techniques (NLP) can be used to identify offensive language and sentiment. However, detecting cyberbullying in image-based data is a major challenge as images do not have a clear textual representation. Hence, bullies often try to bypass existing cyberbullying detection techniques by using images and multi-modal data. We proposed a deep learning technique named as CNBD-Combinational Network for Bullying Detection (CNBD), which is a combination of two networks: Binary Encoder Image Transformer (BEiT) and Multi-Layer Perceptron (MLP) network. To improve the performance of the CNBD, we supplied two additional input factors to the CNBD using concepts called Image Captioning(IC) and OCR (Optical Character Recognition) to extract text overlayed on the images. The experimental results proved the two additional factors gave an advantage to the CNBD technique in terms of accuracy, precision, and recall.