Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network
This work addresses the problem of detecting spam images for email security, but it is incremental as it applies existing CNN and XAI methods to this domain.
The authors tackled image spam detection by combining a Convolutional Neural Network (CNN) for classification with post-hoc Explainable AI (XAI) methods like LIME and SHAP to interpret the model's decisions, achieving satisfactory detection results on a dataset of 6636 images from public email corpora.
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.