Relationship between Uncertainty in DNNs and Adversarial Attacks
This is an incremental review that addresses the problem of understanding and mitigating uncertainty in DNNs for researchers and practitioners in machine learning and security.
The paper reviews the relationship between uncertainty in deep neural networks (DNNs) and adversarial attacks, focusing on how such attacks can increase model uncertainty, though it does not present new experimental results or specific numerical findings.
Deep Neural Networks (DNNs) have achieved state of the art results and even outperformed human accuracy in many challenging tasks, leading to DNNs adoption in a variety of fields including natural language processing, pattern recognition, prediction, and control optimization. However, DNNs are accompanied by uncertainty about their results, causing them to predict an outcome that is either incorrect or outside of a certain level of confidence. These uncertainties stem from model or data constraints, which could be exacerbated by adversarial attacks. Adversarial attacks aim to provide perturbed input to DNNs, causing the DNN to make incorrect predictions or increase model uncertainty. In this review, we explore the relationship between DNN uncertainty and adversarial attacks, emphasizing how adversarial attacks might raise DNN uncertainty.