# Convolutional Neural Networks for Image Spam Detection

arXiv: https://arxiv.org/abs/2204.01710 · HTML: https://mlsanity.com/paper/2204.01710/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 2 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [DeepImageSpam: Deep Learning based Image Spam Detection](https://mlsanity.com/paper/1810.03977/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail](https://mlsanity.com/paper/2210.14616/) (2022)

## Abstract (arXiv)

Spam can be defined as unsolicited bulk email. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply convolutional neural networks (CNN) to this problem, we compare the results obtained using CNNs to other machine learning techniques, and we compare our results to previous related work. We consider both real-world image spam and challenging image spam-like datasets. Our results improve on previous work by employing CNNs based on a novel feature set consisting of a combination of the raw image and Canny edges.