基于DFT的MRI脑成像对抗攻击检测：提升阿尔茨海默病案例研究中的诊断准确性
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies
AI总结：
本研究针对阿尔茨海默病MRI脑成像的对抗攻击问题，提出结合CNN自编码器与二维傅里叶变换的检测防御方法，可有效缓解多种攻击、提升模型鲁棒性。
摘要：
深度学习的最新进展，尤其是在医学成像领域，极大地推动了医疗系统的发展。然而，鉴于医学图像的实际应用及其对个人健康的深远影响，检验其对抗攻击的鲁棒性至关重要。这类攻击可能导致疾病诊断中的分类错误，进而引发严重后果。众多研究既探讨了医学图像上对抗攻击的实施方式，也研究了针对这些威胁的防御机制开发，凸显了深度神经网络易受此类对抗行为攻击的脆弱性。本研究调查了针对阿尔茨海默病相关图像的对抗攻击，并提出了一种抵御这些攻击的防御方法。具体而言，我们研究了对阿尔茨海默病图像采用频域变换的对抗攻击，以及其他知名的对抗攻击类型。我们的方法利用基于卷积神经网络（CNN）的自编码器架构，结合图像的二维傅里叶变换来实现检测目的。仿真结果表明，我们的检测与防御机制可有效缓解多种对抗攻击，从而提升深度神经网络抵御此类漏洞的鲁棒性。
英文摘要：
Recent advancements in deep learning, particularly in medical imaging, have significantly propelled the progress of healthcare systems. However, examining the robustness of medical images against adversarial attacks is crucial due to their real-world applications and profound impact on individuals' health. These attacks can result in misclassifications in disease diagnosis, potentially leading to severe consequences. Numerous studies have explored both the implementation of adversarial attacks on medical images and the development of defense mechanisms against these threats, highlighting the vulnerabilities of deep neural networks to such adversarial activities. In this study, we investigate adversarial attacks on images associated with Alzheimer's disease and propose a defensive method to counteract these attacks. Specifically, we examine adversarial attacks that employ frequency domain transformations on Alzheimer's disease images, along with other well-known adversarial attacks. Our approach utilizes a convolutional neural network (CNN)-based autoencoder architecture in conjunction with the two-dimensional Fourier transform of images for detection purposes. The simulation results demonstrate that our detection and defense mechanism effectively mitigates several adversarial attacks, thereby enhancing the robustness of deep neural networks against such vulnerabilities.
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