Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is important to consider the adversarial use of DNNs in manipulating sensitive data that is crucial to patient healthcare. In this work, we present the design and implementation of a DNN-based image translation attack on biomedical imagery. More specifically, we propose Jekyll, a neural style transfer framework that takes as input a biomedical image of a patient and translates it to a new image that indicates an attacker-chosen disease condition. The potential for fraudulent claims based on such generated 'fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities. We show that these attacks manage to mislead both medical professionals and algorithmic detection schemes. Lastly, we also investigate defensive measures based on machine learning to detect images generated by Jekyll.

翻译：深入神经网络(DNNs)的进步在医疗领域显示出巨大的希望。 但是,帮助这个领域的深层次学习工具也可以用来对抗这个领域。 鉴于医疗领域的欺诈行为十分普遍,必须考虑DNNs在操纵对病人保健至关重要的敏感数据时的对抗性使用。在这项工作中,我们介绍DNN对生物医学图像的图像翻译攻击的设计与实施。更具体地说,我们提议Jekyll是一个神经风格传输框架,将病人的生物医学图像作为输入,并将其转化为显示攻击者选择的疾病状况的新图像。基于这种生成的“假”医学图像的欺诈性索赔的可能性很大,我们展示了对X射线和Retinal Fundus图像模式的成功攻击。我们展示了这些袭击能够误导医疗专业人员和算法探测计划。最后,我们还根据机器学习来调查防御措施,以探测Jekyll产生的图像。