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arXiv · 1807.02905

Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks

Saeid Asgari Taghanaki·Arkadeep Das·Ghassan Hamarneh
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Abstract

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.

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Saeid Asgari Taghanaki, Arkadeep Das, Ghassan Hamarneh. 2018-07-09. Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks. https://arxiv.org/abs/1807.02905

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