Cross-Lingual and Multimodal Cyberbullying and Bias Detection and Content Generation via CyberGenDet

The more digital interactions occur, the greater the demand for effective mechanisms to discourage cyberbullying and bias. CyberGenDet uses OpenAI’s state-of-the-art technologies to detect and generate content in various languages and formats: text, images, and videos. This web application is unique in that, for the first time, it employs a jailbreaking technique to overcome the ethical limitations imposed on AI, enabling the creation of rich synthetic datasets that closely mimic real-world bias and cyberbullying dynamics. CyberGenDet achieves superior detection accuracy and operational flexibility by integrating multimodal AI with advanced transformer-based architectures. Moreover, its cross-lingual performance ensures efficacy across multiple linguistic and cultural settings, making it a key tool for researchers and practitioners working toward a safer online environment. In evaluations using both synthetic and real-world datasets, CyberGenDet achieved a high average detection accuracy, significantly outperforming single-modality detection systems.