Attacking medical images with minimal noise: exploiting vulnerabilities in medical deep-learning systems.

ABSTRACT:

Background

A change in the output of deep neural networks (DNNs) via the perturbation of a few pixels of an image is referred to as an adversarial attack, and these perturbed images are known as adversarial samples. This study examined strategies for compromising the integrity of DNNs under stringent conditions, specifically by inducing the misclassification of medical images of disease with minimal pixel modifications.

Methods

This study used the following three publicly available datasets: the chest radiograph of emphysema (cxr) dataset, the melanocytic lesion (derm) dataset, and the Kaggle diabetic retinopathy (dr) dataset. To attack the medical images, we proposed a method termed decrease group differential evolution (DGDE) for generating adversarial images. Under this m

SUBMITTER: Wang W 

PROVIDER: S-EPMC11651935 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature