  Image spam emails are often used to evade text-based spam filters that detect
spam emails with their frequently used keywords. In this paper, we propose a
new image spam email detection tool called DeepCapture using a convolutional
neural network (CNN) model. There have been many efforts to detect image spam
emails, but there is a significant performance degrade against entirely new and
unseen image spam emails due to overfitting during the training phase. To
address this challenging issue, we mainly focus on developing a more robust
model to address the overfitting problem. Our key idea is to build a
CNN-XGBoost framework consisting of eight layers only with a large number of
training samples using data augmentation techniques tailored towards the image
spam detection task. To show the feasibility of DeepCapture, we evaluate its
performance with publicly available datasets consisting of 6,000 spam and 2,313
non-spam image samples. The experimental results show that DeepCapture is
capable of achieving an F1-score of 88%, which has a 6% improvement over the
best existing spam detection model CNN-SVM with an F1-score of 82%. Moreover,
DeepCapture outperformed existing image spam detection solutions against new
and unseen image datasets.
