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. 2021 Jun 7;21(11):3922.
doi: 10.3390/s21113922.

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Sheeba Lal  1 , Saeed Ur Rehman  1 , Jamal Hussain Shah  1 , Talha Meraj  1 , Hafiz Tayyab Rauf  2 , Robertas Damaševičius  3 , Mazin Abed Mohammed  4 , Karrar Hameed Abdulkareem  5
Affiliations

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Sheeba Lal et al. Sensors (Basel). .

Abstract

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.

Keywords: adversarial attack; adversarial training; deep learning; diabetic retinopathy; feature fusion; speckle-noise attack.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Block diagram of the proposed system.
Figure 2
Figure 2
Addition of FGSM Attacks. The first row shows the original images, while the second row represents the FGSM attacked images that mislead the model.
Figure 3
Figure 3
Addition of FGSM attacks. The first row shows the original images, while the second row represents the SN attacked images.
Figure 4
Figure 4
Addition of DF attacks. The first row shows the original images, while the second row indicates the DF attacked images.
Figure 5
Figure 5
Illustration of the adversarial training process.
Figure 6
Figure 6
ROC curve (left) and fusion scatter plot (right).
See this image and copyright information in PMC

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