In 2018, clinics and hospitals were hit with numerous attacks leading to significant data breaches and interruptions in medical services. An attacker with access to medical records can do much more than hold the data for ransom or sell it on the black market. In this paper, we show how an attacker can use deep-learning to add or remove evidence of medical conditions from volumetric (3D) medical scans. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. We implement the attack using a 3D conditional GAN and show how the framework (CT-GAN) can be automated. Although the body is complex and 3D medical scans are very large, CT-GAN achieves realistic results which can be executed in milliseconds. To evaluate the attack, we focused on injecting and removing lung cancer from CT scans. We show how three expert radiologists and a state-of-the-art deep learning AI are highly susceptible to the attack. We also explore the attack surface of a modern radiology network and demonstrate one attack vector: we intercepted and manipulated CT scans in an active hospital network with a covert penetration test. Demo video: this https URL Source code: this https URL
Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the sensor noise analysis. We built the reference noise pattern for each CT-Scanner from its 3D image, then we correlated the tested 3D images with each reference noise pattern in order to identify the corresponding CT-Scanner. We used a wavelet-based Wiener filter approach to extract the noise. Experimental results were applied on eight 3D images of 100 slices from different CT-Scanners, and we were able to identify each CT-Scanner separately. In this paper, we focus on the “blind” identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94 and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.