NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks
Convolution Neural Network (CNN) models have gained ground in research activities particularly in medical images used for Diabetes Retinopathy (DR) detection. X-ray, MRI, and CT scans have all been used to validate CNN models, with classification accuracy generally reaching that of trained doctors....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9903611/ |
| _version_ | 1811227154056216576 |
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| author | Othmane Daanouni Bouchaib Cherradi Amal Tmiri |
| author_facet | Othmane Daanouni Bouchaib Cherradi Amal Tmiri |
| author_sort | Othmane Daanouni |
| collection | DOAJ |
| description | Convolution Neural Network (CNN) models have gained ground in research activities particularly in medical images used for Diabetes Retinopathy (DR) detection. X-ray, MRI, and CT scans have all been used to validate CNN models, with classification accuracy generally reaching that of trained doctors. It is mandatory to evaluate the strength of CNN models used in medical tasks against adversarial attacks especially in healthcare, that is to say, the security of such models is becoming extremely relevant to the diagnosis as this latter will guide high-stakes decision-making. However, little study has been conducted to better comprehend this issue. This paper focuses on MobileNet CNN architecture in order to investigate its vulnerability against fast gradient sign methods (FGSM) adversarial attacks. For this end, a Neural Structure Learning (NSL) and a Multi-Head Attention (MHA) have been used to effectively reduce the vulnerability against attack by end-to-end CNN training with adversarial neighbors that produce adversarial perturbations on optical coherence tomography (OCT) images. With suggested model NSL-MHA-CNN, there has been an ability to maintain model performance on adversarial attack without increasing cost of training. Through theoretical assistance and empirical validation, it was possible to examine the stability of MobileNet architecture and demonstrate its susceptibility, particularly to adversarial attack. The experiments in this paper show that indiscernible degrees of perturbation <inline-formula> <tex-math notation="LaTeX">$\varepsilon < 0.01$ </tex-math></inline-formula> were sufficient to cause a task failure resulting to misclassification in majority of the time. Moreover, empirical simulation shows that the proposed approach advanced in this paper can be an effective method to defense against adversarial attack at level of CNN model testing. |
| first_indexed | 2024-04-12T09:36:30Z |
| format | Article |
| id | doaj.art-30d9464431724ee78ef0665487abd3d6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T09:36:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-30d9464431724ee78ef0665487abd3d62022-12-22T03:38:12ZengIEEEIEEE Access2169-35362022-01-011010398710399910.1109/ACCESS.2022.32101799903611NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial AttacksOthmane Daanouni0https://orcid.org/0000-0002-3094-3726Bouchaib Cherradi1https://orcid.org/0000-0002-2016-8682Amal Tmiri2LaROSERI Laboratory, Chouaib Doukkali University, El Jadida, MoroccoLaROSERI Laboratory, Chouaib Doukkali University, El Jadida, MoroccoLaROSERI Laboratory, Chouaib Doukkali University, El Jadida, MoroccoConvolution Neural Network (CNN) models have gained ground in research activities particularly in medical images used for Diabetes Retinopathy (DR) detection. X-ray, MRI, and CT scans have all been used to validate CNN models, with classification accuracy generally reaching that of trained doctors. It is mandatory to evaluate the strength of CNN models used in medical tasks against adversarial attacks especially in healthcare, that is to say, the security of such models is becoming extremely relevant to the diagnosis as this latter will guide high-stakes decision-making. However, little study has been conducted to better comprehend this issue. This paper focuses on MobileNet CNN architecture in order to investigate its vulnerability against fast gradient sign methods (FGSM) adversarial attacks. For this end, a Neural Structure Learning (NSL) and a Multi-Head Attention (MHA) have been used to effectively reduce the vulnerability against attack by end-to-end CNN training with adversarial neighbors that produce adversarial perturbations on optical coherence tomography (OCT) images. With suggested model NSL-MHA-CNN, there has been an ability to maintain model performance on adversarial attack without increasing cost of training. Through theoretical assistance and empirical validation, it was possible to examine the stability of MobileNet architecture and demonstrate its susceptibility, particularly to adversarial attack. The experiments in this paper show that indiscernible degrees of perturbation <inline-formula> <tex-math notation="LaTeX">$\varepsilon < 0.01$ </tex-math></inline-formula> were sufficient to cause a task failure resulting to misclassification in majority of the time. Moreover, empirical simulation shows that the proposed approach advanced in this paper can be an effective method to defense against adversarial attack at level of CNN model testing.https://ieeexplore.ieee.org/document/9903611/Fast gradient sign methodmalicious attackMobileNetmulti-head attentionneural structure learningvulnerability in CNN |
| spellingShingle | Othmane Daanouni Bouchaib Cherradi Amal Tmiri NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks IEEE Access Fast gradient sign method malicious attack MobileNet multi-head attention neural structure learning vulnerability in CNN |
| title | NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks |
| title_full | NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks |
| title_fullStr | NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks |
| title_full_unstemmed | NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks |
| title_short | NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks |
| title_sort | nsl mha cnn a novel cnn architecture for robust diabetic retinopathy prediction against adversarial attacks |
| topic | Fast gradient sign method malicious attack MobileNet multi-head attention neural structure learning vulnerability in CNN |
| url | https://ieeexplore.ieee.org/document/9903611/ |
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