@inproceedings{f3f7fa69ecf049ddb670e9a5a1cd49b6,
title = "Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network",
abstract = "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 N eural 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.",
keywords = "Convolutional Neural Network (CNN), Cyber Security, Deep Learning, Explainable Artificial Intelligence (XAI), Image Spam",
author = "Zhibo Zhang and Ernesto Damiani and Hamadi, {Hussam Al} and Yeun, {Chan Yeob} and Fatma Taher",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Cyber Resilience, ICCR 2022 ; Conference date: 06-10-2022 Through 07-10-2022",
year = "2022",
doi = "10.1109/ICCR56254.2022.9995839",
language = "British English",
series = "International Conference on Cyber Resilience, ICCR 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "International Conference on Cyber Resilience, ICCR 2022",
address = "United States",
}