Image based Spam Detection using Recurrent Neural Networks (RNN)

Abstract

Image-spam initially arose as a way of bypassing text-based spam filters. It is widely used to advertise products, mislead individuals into providing personal information, or transmit hazardous viruses. Image spam is harder to detect than text-based spam. Image-based encryption methods can be used to create image spam that is even more difficult to detect than what is often seen in reality. Image spam has evolved over time and may now overcome various kinds of classic anti-spam methods. Spammers can utilise pictures that just include text, sliced images, and randomly created images. Text-only images were used in the initial generation of image spam. Such images are practically empty, containing only pure text. Such text can be retrieved using optical character recognition (OCR), and then processed using normal text-based filters