Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases
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Citações na Scopus
2
Tipo de produção
conferenceObject
Data de publicação
2022
Título da Revista
ISSN da Revista
Título do Volume
Editora
SPIE-INT SOC OPTICAL ENGINEERING
Autores
AGUIAR, Erikson J. de
MARCOMINI, Karem D.
QUIRINO, Felipe A.
TRAINA JR., Caetano
TRAINA, Agma J. M.
Citação
MEDICAL IMAGING 2022: COMPUTER-AIDED DIAGNOSIS, v.12033, article ID 120332P, 7p, 2022
Resumo
The SARS-CoV-2 (COVID-19) disease rapidly spread worldwide, thus increasing the need to create new strategies to fight it. Several researchers in different fields have attempted to develop methods to early identifying it and mitigating its effects. The Deep Learning (DL) approach, such as the Convolutional Neural Networks (CNNs), has been increasingly used in COVID-19 diagnoses. These models intend to support decision-making and are doing well to detecting patient status early. Although DL models have good accuracy to support diagnosis, they are vulnerable to Adversarial Attacks. These attacks are new methods to make DL models biased by adding small perturbations on the original image. This paper investigates the impact of Adversarial Attacks on DL models for classifying X-ray images of COVID-19 cases. We focused on the attack Fast Gradient Sign Method (FGSM), which aims to add perturbations to the testing images by combining a perturbation matrix, producing a crafted image. We conduct the experiments analyzing the model's performance attack-free and adding attacks. The following CNNs models were selected: DenseNet201, ResNet-50V2, MobileNetV2, NasNet and VGG16. In the attack-free environment, we reach precision around 99%. When it adds the attack, our results revealed that all models suffer from performance reduction, and the most affected was MobileNet that reduced its ability from 98.61% to 67.73%. However, the VGG16 network showed to be the least affected by the attacks. Our finds describe that DL models for COVID-19 are vulnerable to Adversarial Examples. The FGSM was capable of fooling the model, resulting in a significant reduction in the DL performance.
Palavras-chave
Adversarial attacks, deep neural networks, COVID-19, Fast Gradient Sign Method
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