An Improved Image Spam Classification Model Based on Deep Learning Techniques

Abstract

Image Spam is a type of spam that has embedded text in an image. Classification of Image Spam is done using various machine learning approaches based on a broad set of features extracted from the image. For its remarkable results, the convolutional neural networks (CNN) are widely used in image classification as well as feature extraction tasks. In this research, we analyze image spam using a CNN model based on deep learning techniques. The proposed model is fine-tuned and optimized for both feature extraction as well as for classification tasks. We also compared our proposed model to different "Improved"and "Challenge"image spam datasets, which were developed for increasing the difficulty of the classification task. Our model significantly improves the accuracy of the classification task as compared to other approaches on the same datasets.