DeepImageSpam: Deep Learning based Image Spam Detection

TL;DR

This paper introduces a deep learning approach using convolutional neural networks for detecting image spam, achieving high accuracy and outperforming traditional methods.

Contribution

It presents a novel CNN-based method for image spam detection and evaluates it on a new dataset, demonstrating superior performance.

Findings

Achieved 91.7% accuracy in image spam detection

Outperformed existing image processing and machine learning techniques

Used a dataset of 810 natural and 928 spam images

Abstract

Hackers and spammers are employing innovative and novel techniques to deceive novice and even knowledgeable internet users. Image spam is one of such technique where the spammer varies and changes some portion of the image such that it is indistinguishable from the original image fooling the users. This paper proposes a deep learning based approach for image spam detection using the convolutional neural networks which uses a dataset with 810 natural images and 928 spam images for classification achieving an accuracy of 91.7% outperforming the existing image processing and machine learning techniques