Log In Sign Up

Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks

by   Hokuto Hirano, et al.

Under the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has been advanced, to rapidly and accurately detect COVID-19 cases, because the need for expert radiologists, who are limited in number, forms a bottleneck for the screening. However, so far, the vulnerability of DNN-based systems has been poorly evaluated, although DNNs are vulnerable to a single perturbation, called universal adversarial perturbation (UAP), which can induce DNN failure in most classification tasks. Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs generated using simple iterative algorithms. We consider nontargeted UAPs, which cause a task failure resulting in an input being assigned an incorrect label, and targeted UAPs, which cause the DNN to classify an input into a specific class. The results demonstrate that the models are vulnerable to nontargeted and targeted UAPs, even in case of small UAPs. In particular, 2 norm of an image in the image dataset achieves >85 the nontargeted and targeted attacks, respectively. Due to the nontargeted UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The targeted UAPs make the DNN models classify most chest X-ray images into a given target class. The results indicate that careful consideration is required in practical applications of DNNs to COVID-19 diagnosis; in particular, they emphasize the need for strategies to address security concerns. As an example, we show that iterative fine-tuning of the DNN models using UAPs improves the robustness of the DNN models against UAPs.


page 7

page 8

page 10

page 11

page 12


Simple iterative method for generating targeted universal adversarial perturbations

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In pa...

Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 Models

Early identification of COVID-19 using a deep model trained on Chest X-R...

A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening

We introduce a comprehensive screening platform for the COVID-19 (a.k.a....

Simple black-box universal adversarial attacks on medical image classification based on deep neural networks

Universal adversarial attacks, which hinder most deep neural network (DN...

Bias Field Poses a Threat to DNN-based X-Ray Recognition

The chest X-ray plays a key role in screening and diagnosis of many lung...

On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices

Vulnerability to adversarial attacks is a well-known weakness of Deep Ne...

COVID-19 detection using chest X-rays: is lung segmentation important for generalization?

We evaluated the generalization capability of deep neural networks (DNNs...

Code Repositories