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Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

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Abstract

Deep and machine learning models have become pivotal in medical image analysis, especially for diagnosing COVID-19 using X-rays and CT scans. While these models, including transfer learning-based approaches, have achieved high accuracy, they remain highly vulnerable to adversarial attacks, which can manipulate input data and cause misclassification, posing critical risks in clinical applications. This study introduces a novel approach to addressing this issue by systematically evaluating the impact of adversarial attacks on COVID-19 diagnosis models built with two leading architectures, VGG-16 and DenseNet-121, using the Fast Gradient Sign Method (FGSM). The FGSM attack causes a dramatic drop in accuracy, reducing VGG-16’s accuracy from 95.12 to 9.97% and DenseNet-121’s from 96.51 to 10.13%. To counter these vulnerabilities, we propose a novel defense mechanism that combines adversarial training with Gaussian noise data augmentation, a dynamic approach that generates perturbations across various epsilon values during the training phase. This innovative method significantly enhances model robustness, restoring accuracy to over 92% on adversarial examples. These findings emphasize the need for strong defense mechanisms in deep learning models for COVID-19 diagnosis, ensuring reliability and security against adversarial threats in clinical environments.

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Data availability

The datasets analyzed during the current study are available in the KAGGLE repository, https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md. The code generated during and/or analyzed during the current study is available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, India

    Sheikh Burhan Ul Haque & Sheikh Moeen Ul Haque

  2. Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India

    Aasim Zafar & Khushnaseeb Roshan

  3. Department of Computer Science Engineering, Guru Nanak Institution Ibrahimpatnam, Hyderabad, Telangana, 501506, India

    Sheikh Riyaz Ul Haq

  4. Department of Electronics and Communication, Guru Nanak Institution Ibrahimpatnam, Hyderabad, Telangana, 501506, India

    Mohassin Ahmad

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  1. Sheikh Burhan Ul Haque
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Haque, S.B.U., Zafar, A., Haq, S.R.U. et al. Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis. Soft Comput 29, 1879–1896 (2025). https://doi.org/10.1007/s00500-025-10516-z

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