Visual-based anti spamming techniques with learning

In today's world, email remains a primary medium for information exchange. However, the flood of spam emails poses significant challenges to users, demanding innovative solutions to ensure a secure and efficient communication environment. This thesis recognizes the evolving landscape of spam, where spammers employ tricks to outsmart traditional spam filters, highlighting the need for advanced defense mechanisms. The proposed system introduces a visionary approach to anti-spamming techniques, leveraging computer vision and machine learning methodologies to emulate the human visual perception of emails. The multi-step process mimics the human eye's natural way of processing visual information, automatically rendering emails to capture their visual content. Following this, text extraction using Optical Character Recognition (OCR) and classification with a Naive Bayes (NB) classifier are employed to distinguish between spam and ham based on textual content. To augment the system's accuracy, a Convolutional Neural Network (CNN) is introduced to analyze and classify screenshots of the email content. The CNN, serving as a visual perception model, learns patterns and features crucial for differentiating between spam and legitimate emails. In response to the dynamic nature of spamming techniques, the proposed solution uses meta classifier that integrates both the text-based NB classifier and the image-based CNN concurrently. This approach enhances the system's adaptability ,providing a robust and comprehensive decision-making mechanism.