  Deep learning models have been used in creating various effective image
classification applications. However, they are vulnerable to adversarial
attacks that seek to misguide the models into predicting incorrect classes. Our
study of major adversarial attack models shows that they all specifically
target and exploit the neural networking structures in their designs. This
understanding makes us develop a hypothesis that most classical machine
learning models, such as Random Forest (RF), are immune to adversarial attack
models because they do not rely on neural network design at all. Our
experimental study of classical machine learning models against popular
adversarial attacks supports this hypothesis. Based on this hypothesis, we
propose a new adversarial-aware deep learning system by using a classical
machine learning model as the secondary verification system to complement the
primary deep learning model in image classification. Although the secondary
classical machine learning model has less accurate output, it is only used for
verification purposes, which does not impact the output accuracy of the primary
deep learning model, and at the same time, can effectively detect an
adversarial attack when a clear mismatch occurs. Our experiments based on
CIFAR-100 dataset show that our proposed approach outperforms current
state-of-the-art adversarial defense systems.
