Multi-Modal Content Filtration and Secure Communication System
Abstract: Multi-Modal Content Filtering and Secure Communication System integrates advanced technologies like cryptography, malicious URL detection using Random Forest, and hate speech detection using NLP and BiLSTM models. Additionally, the system offers multi-factor authentication with face recognition for added security. The project's main goal is to provide users with a secure and user-friendly communication platform. By employing hybrid cryptography techniques, the system ensures end-to-end encryption, safeguarding the confidentiality and integrity of all communication. Advanced machine learning algorithms help detect and block harmful content, including hate speech and malicious URLs, in real-time. With multifactor authentication options (Basic, Two Factor, and Three Factor), users can choose their preferred level of security. The inclusion of face recognition adds an extra layer of protection against unauthorized access. In conclusion, the "Multi-Modal Content Filtering and Secure Communication System" offers a comprehensive solution for secure communication, protecting users from cyber threats and promoting a safer online environment.
Keywords: BiLSTM, NLP, CNN, RandomForest
How to Cite?: Bindu B, "Multi-Modal Content Filtration and Secure Communication System", Volume 14 Issue 4, April 2025, International Journal of Science and Research (IJSR), Pages: 2101-2104, https://www.ijsr.net/getabstract.php?paperid=SR25424100601, DOI: https://dx.doi.org/10.21275/SR25424100601