GReAT: A Graph Regularized Adversarial Training Method
Samet Bayram Kenneth Barner
This paper proposes a regularization method called GReAT, Graph Regularized
Adversarial Training, to improve deep learning models' classification
performance. Adversarial examples are a well-known challenge in machine
learning, where small, purposeful perturbations to input data can mislead
models. Adversarial training, a powerful and one of the most effective defense
strategies, involves training models with both regular and adversarial
examples. However, it often neglects the underlying structure of the data. In
response, we propose GReAT, a method that leverages data graph structure to
enhance model robustness. GReAT deploys the graph structure of the data into
the adversarial training process, resulting in more robust models that better
generalize its testing performance and defend against adversarial attacks.
Through extensive evaluation on benchmark datasets, we demonstrate GReAT's
effectiveness compared to state-of-the-art classification methods, highlighting
its potential in improving deep learning models' classification performance.