Recent advancements in state-of-the-art technologies, including Artificial Intelligence (AI), Internet of Things (IoT), and cloud computing, have led to the emergence of an innovative technology known as digital twins (DTs). A digital twin is a virtual replica of the physical entity, with data connections in between. This technology has proven highly effective in several industries by improving decision-making and operational efficiency. In critical areas like healthcare, digital twins are increasingly being used to address the limitations of conventional approaches by creating virtual simulations of hospitals, medical equipment, patients, or even individual organs. These medical digital twins (MDT) revolutionize the healthcare industry by offering advanced solutions to enhance treatment outcomes and overall patient care. However, these systems are challenging because of the security and critical issues involved. Therefore, despite their achievements, the numerous security threats make it crucial to address the security challenges of digital twin technology. Given the lack of research on attacks targeting MDT functionalities, we concentrated on a specific cyber threat called adversarial attacks. Adversarial attacks exploit the model’s performance by introducing small, carefully crafted perturbations to manipulate the input data. To assess the vulnerability of medical digital twins to such attacks, we carried out a proof-of-concept study. Using image processing techniques and an artificial neural network model, we created a digital twin to diagnose breast cancer through thermography images. Then, we employed this digital twin to initiate an adversarial attack. For this purpose, we inserted adversarial perturbation as input to the trained model. Our results demonstrated the vulnerability of the digital twin model to adversarial attacks. To tackle this problem, we implemented an innovative modification to the digital twin’s architecture to enhance its robustness against various attacks. We proposed a novel defense method that fuses wavelet denoising and adversarial training, substantially strengthening the model’s resilience to adversarial attacks. Furthermore, the proposed digital twin is evaluated using a dataset of diabetic foot ulcers. To the best of our knowledge, it is the first defense method that makes the medical digital twin significantly robust against adversarial attacks.

中文翻译：

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医疗数字孪生中的对抗鲁棒图像处理


人工智能 （AI）、物联网 （IoT） 和云计算等最先进技术的最新进展导致了一种称为数字孪生 （DT） 的创新技术的出现。数字孪生是物理实体的虚拟副本，两者之间有数据连接。这项技术通过提高决策和运营效率，在多个行业中被证明非常有效。在医疗保健等关键领域，数字孪生越来越多地用于通过创建医院、医疗设备、患者甚至单个器官的虚拟模拟来解决传统方法的局限性。这些医疗数字孪生 （MDT） 通过提供先进的解决方案来改善治疗结果和整体患者护理，从而彻底改变了医疗保健行业。但是，由于涉及安全性和关键问题，这些系统具有挑战性。因此，尽管他们取得了成就，但众多安全威胁使得应对数字孪生技术的安全挑战变得至关重要。由于缺乏对针对 MDT 功能的攻击的研究，我们专注于一种称为对抗性攻击的特定网络威胁。对抗性攻击通过引入精心设计的小型扰动来操纵输入数据，从而利用模型的性能。为了评估医疗数字孪生对此类攻击的脆弱性，我们进行了一项概念验证研究。使用图像处理技术和人工神经网络模型，我们创建了一个数字孪生，通过热成像图像诊断乳腺癌。然后，我们利用这个数字孪生来发起对抗性攻击。为此，我们插入了对抗性扰动作为训练模型的输入。 我们的结果表明，数字孪生模型容易受到对抗性攻击。为了解决这个问题，我们对数字孪生的架构进行了创新修改，以增强其抵御各种攻击的稳健性。我们提出了一种新的防御方法，融合了小波去噪和对抗性训练，大大增强了模型对对抗性攻击的弹性。此外，使用糖尿病足溃疡数据集评估了所提出的数字孪生。据我们所知，这是第一种使医疗数字孪生对对抗性攻击具有显著稳健性的防御方法。