Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks has shown the ease of designing samples to mislead a model into making incorrect predictions. In this work, we propose a model agnostic explainability-based method for the accurate detection of adversarial samples on two datasets with different complexity and properties: Electronic Health Record (EHR) and chest X-ray (CXR) data. On the MIMIC-III and Henan-Renmin EHR datasets, we report a detection accuracy of 77% against the Longitudinal Adversarial Attack. On the MIMIC-CXR dataset, we achieve an accuracy of 88%; significantly improving on the state of the art of adversarial detection in both datasets by over 10% in all settings. We propose an anomaly detection based method using explainability techniques to detect adversarial samples which is able to generalise to different attack methods without a need for retraining.

中文翻译：

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基于可解释机器学习的医学数据攻击不可知对抗检测

可解释的机器学习已变得越来越普遍，尤其是在医疗保健中，可解释的模型对于道德和可信赖的自动化决策至关重要。深度学习模型对对抗性攻击的敏感性研究表明，设计样本容易使模型误导做出错误的预测。在这项工作中，我们提出了一种基于模型不可知论性的方法，用于在两个具有不同复杂性和属性的数据集上准确检测对抗性样本：电子健康记录（EHR）和胸部X射线（CXR）数据。在MIMIC-III和河南-人民EHR数据集上，我们报告了针对纵向对抗性攻击的检测精度为77％。在MIMIC-CXR数据集上，我们达到了88％的准确度；在所有设置中，两个数据集中的对抗性检测技术水平均显着提高了10％以上。我们提出一种基于异常检测的方法，该方法使用可解释性技术来检测对抗性样本，该样本能够推广到不同的攻击方法而无需重新训练。