Images Spam Detection on Online Social Media using CNN with Pre-Trained Model

Abstract

Abstract: Nowadays attackers move to image spam techniques instead of text based. The Majority of conventional techniques solely possess the capability to identify spam confined to textual content and hyperlinks. In this research we have done “Deep Convolutional Neural Networks” (DCNNs)in conjunction with pre-trained architectures. Image classification is one of the areas that has increased in the last decade very rapidly. But due to the less computational resources it become very challenging to train a good image classification model. With the help of Transfer learning, we can overcome this type of situation and can build a good image classification model. Our proposed model utilizes deep convolutional neural networks (DCNNs) along with pre-trained architectures. By fine-tuning this pre-trained model on the specific image dataset, the model can effectively learn to detect image- based spam content. Our suggested model surpasses contemporary state-of-the-art detection models concerning both accuracy and efficiency in the realm of the image spam identification.