DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies

TL;DR

This paper presents a novel frequency domain-based CNN autoencoder method to detect and defend against adversarial attacks on MRI brain images, improving diagnostic reliability for Alzheimer's disease.

Contribution

It introduces a Fourier transform-based autoencoder approach for detecting adversarial attacks in medical imaging, specifically targeting Alzheimer's disease MRI scans.

Findings

Effective detection of adversarial attacks using Fourier transform features.

Enhanced robustness of neural networks against various adversarial strategies.

Mitigation of misclassification risks in Alzheimer's diagnosis.

Abstract

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.