  Recently, there have been several successful deep learning approaches for
automatically classifying chest X-ray images into different disease categories.
However, there is not yet a comprehensive vulnerability analysis of these
models against the so-called adversarial perturbations/attacks, which makes
deep models more trustful in clinical practices. In this paper, we extensively
analyzed the performance of two state-of-the-art classification deep networks
on chest X-ray images. These two networks were attacked by three different
categories (ten methods in total) of adversarial methods (both white- and
black-box), namely gradient-based, score-based, and decision-based attacks.
Furthermore, we modified the pooling operations in the two classification
networks to measure their sensitivities against different attacks, on the
specific task of chest X-ray classification.
