Sm Spam Detection

SMS spam detection aims to automatically identify and filter unwanted messages, protecting users from fraud and privacy violations. Current research heavily utilizes transformer-based models like BERT and RoBERTa, leveraging natural language processing techniques to analyze message content and achieve high accuracy rates. This field is significant due to the pervasive nature of SMS spam and ongoing efforts to improve detection accuracy while addressing privacy concerns and developing robust, adversary-resistant systems, often through federated learning approaches. The development of larger, more representative datasets is also a key focus.