CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning

Abstract

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: https://youtu.be/_mkRAArj-x0 Source code: https://github.com/ymirsky/CT-GAN

arXiv commentThis paper is included in the Proceedings of the 28th USENIX Security Symposium (USENIX Security 2019)

References in corpus (8)

• Generative Adversarial Network in Medical Imaging: A Review
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
• ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection
• Learning Implicit Brain MRI Manifolds with Deep Learning
• MelanoGANs: High Resolution Skin Lesion Synthesis with GANs
• Blood Vessel Geometry Synthesis using Generative Adversarial Networks
• Noiseprint: a CNN-based camera model fingerprint
• Deep CT to MR Synthesis using Paired and Unpaired Data

Cited by in corpus (5)

• Generative Adversarial Network in Medical Imaging: A Review
• Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
• Biocybersecurity -- A Converging Threat as an Auxiliary to War
• Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective
• On the Emerging Area of Biocybersecurity and Relevant Considerations