# Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks Source: [Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:08:59.042Z Authors: Saeid Asgari Taghanaki, Arkadeep Das, Ghassan Hamarneh Published: 2018-07-09T00:58:04.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()