# DeepImageSpam: Deep Learning based Image Spam Detection

arXiv: https://arxiv.org/abs/1810.03977 · HTML: https://mlsanity.com/paper/1810.03977/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network](https://mlsanity.com/paper/2209.03166/) (2022)
- [Convolutional Neural Networks for Image Spam Detection](https://mlsanity.com/paper/2204.01710/) (2022)
- [Universal Adversarial Perturbations and Image Spam Classifiers](https://mlsanity.com/paper/2103.05469/) (2021)

## Abstract (arXiv)

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