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
Many works have proposed methods for detecting forgeries in medical images @cite_49 , but none have focused on the attack itself. The most common methods of image forgery are: copying content from one image to another (image splicing), duplicating content within the same image to cover up or add something (copy-move), and enhancing an image to give it a different feel (image retouching) @cite_21 .
@cite_21 @cite_49
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Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks. Abstract Editing a real-world photo through computer software or mobile applications is one of the easiest things one can do today before sharing the doctored image on one’s social networking sites. Although most people do it for fun, it is suspectable if one concealed an object or changed someone’s face within the image. Before questioning the intention behind the editing operations, we need to first identify how and which part of the image has been manipulated. It therefore demands automatic tools for identifying the intrinsic difference between authentic images and tampered images. This survey provides an overview on typical image tampering types, released image tampering datasets and recent tampering detection approaches. It presents a distinct perspective to rethink various assumptions of tampering clues behind different detection approaches. And this further encourages the research community to develop general tampering localization methods in the future instead of adhering to single-type tampering detection.