MedDefend: Securing Medical IoT With Adaptive Noise-Reduction-Based Adversarial Detection
Nirmal Joseph, Sudhish N. George, P. M. Ameer, Kiran Bylappa Raja · IEEE Internet of Things Journal · 2024
Despite their exceptional performance in the Internet of Medical Things (IoMT), deep learning models are susceptible to adversarial attacks. Existing defense approaches often suffer from limitations, including required model alterations, attack-type awareness, and high-computational complexity. This article introduces MedDefend: a lightweight, three-way detection technique, combining adaptive noise injection with a tailored robust principal component analysis (t-RPCA)-based noise mitigation. Initially, the method employs strategic image- dependent Gaussian noise injection, guided by class activation maps, to mask adversarial perturbations. Subsequently, t-RPCA is employed to eliminate the introduced noise along with the adversarial perturbations. Finally, the model evaluates classification consistency between original and denoised samples to detect potential adversarial examples. MedDefend effectively detects adversarial attacks across various medical imaging modalities, with a lightweight, training-free, and model-agnostic design suitable for IoMT integration. To encourage community engagement and reimplementation, our code is available athttps://github.com/nirmalpadichira/MedDefend/tree/main.