# Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network

arXiv: https://arxiv.org/abs/2209.03166 · HTML: https://mlsanity.com/paper/2209.03166/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [Explainable Face Recognition](https://mlsanity.com/paper/2008.00916/) (2020)
- [DeepImageSpam: Deep Learning based Image Spam Detection](https://mlsanity.com/paper/1810.03977/) (2018)

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

- [Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification](https://mlsanity.com/paper/2401.01896/) (2023)
- [Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence](https://mlsanity.com/paper/2402.07915/) (2024)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (2023)

## Abstract (arXiv)

Image spam threat detection has continually been a popular area of research with the internet's phenomenal expansion. This research presents an explainable framework for detecting spam images using Convolutional Neural Network(CNN) algorithms and Explainable Artificial Intelligence (XAI) algorithms. In this work, we use CNN model to classify image spam respectively whereas the post-hoc XAI methods including Local Interpretable Model Agnostic Explanation (LIME) and Shapley Additive Explanations (SHAP) were deployed to provide explanations for the decisions that the black-box CNN models made about spam image detection. We train and then evaluate the performance of the proposed approach on a 6636 image dataset including spam images and normal images collected from three different publicly available email corpora. The experimental results show that the proposed framework achieved satisfactory detection results in terms of different performance metrics whereas the model-independent XAI algorithms could provide explanations for the decisions of different models which could be utilized for comparison for the future study.