Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
This addresses safety concerns for deploying medical AI systems in clinical settings, though it is incremental as it builds on existing adversarial attack research.
The paper investigates adversarial attacks on deep learning systems for medical image analysis, finding that these models are more vulnerable than those for natural images, but attacks can be detected with over 98% AUC using simple detectors.
Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems.