  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.
