Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network
Source of Publication
2022 International Conference on Cyber Resilience (ICCR)
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.
Measurement, Additives, Unsolicited e-mail, Closed box, Classification algorithms, Convolutional neural networks, Artificial intelligence
Zhang, Zhibo; Damiani, Ernesto; Hamadi, Hussam Al; Yeun, Chan Yeob; and Taher, Fatma, "Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network" (2022). All Works. 5581.
Indexed in Scopus
Open Access Type
Green: A manuscript of this publication is openly available in a repository