Intelligent image synthesis to attack a segmentation CNN using adversarial learning

TL;DR

This paper introduces a novel adversarial attack method on CNN-based medical image segmentation models that generates realistic, anatomically varied adversarial examples without prior specification, effectively reducing segmentation accuracy.

Contribution

The paper presents a new approach for creating realistic, deformable adversarial examples that attack CNN segmentation models without predefining the attack, enhancing understanding of model vulnerabilities.

Findings

Effective attack on multiple CNN segmentation models

Adversarial examples cause significant Dice score reduction

Generates anatomically plausible perturbations

Abstract

Deep learning approaches based on convolutional neural networks (CNNs) have been successful in solving a number of problems in medical imaging, including image segmentation. In recent years, it has been shown that CNNs are vulnerable to attacks in which the input image is perturbed by relatively small amounts of noise so that the CNN is no longer able to perform a segmentation of the perturbed image with sufficient accuracy. Therefore, exploring methods on how to attack CNN-based models as well as how to defend models against attacks have become a popular topic as this also provides insights into the performance and generalization abilities of CNNs. However, most of the existing work assumes unrealistic attack models, i.e. the resulting attacks were specified in advance. In this paper, we propose a novel approach for generating adversarial examples to attack CNN-based segmentation models for medical images. Our approach has three key features: 1) The generated adversarial examples exhibit anatomical variations (in form of deformations) as well as appearance perturbations; 2) The adversarial examples attack segmentation models so that the Dice scores decrease by a pre-specified amount; 3) The attack is not required to be specified beforehand. We have evaluated our approach on CNN-based approaches for the multi-organ segmentation problem in 2D CT images. We show that the proposed approach can be used to attack different CNN-based segmentation models.