CardioDefense: Defending against adversarial attack in ECG classification with adversarial distillation training

Shao, Jiahao; Geng, Shijia; Fu, Zhaoji; Xu, Weilun; Liu, Tong; Hong, Shenda

doi:10.1016/j.bspc.2023.105922

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2023.105922

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CardioDefense: Defending against adversarial attack in ECG classification with adversarial distillation training

Jiahao Shao,

Shijia Geng,

Zhaoji Fu

et al.

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Cited by 3 publications

References 26 publications

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Wave masked autoencoder: An electrocardiogram signal diagnosis model based on wave making strategy

Wang,

Tao

2025

Information Sciences

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Wave masked autoencoder: An electrocardiogram signal diagnosis model based on wave making strategy

Wang,

Tao

2025

Information Sciences

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Learn to Defend: Adversarial Multi-Distillation for Automatic Modulation Recognition Models

Chen,

Wang,

et al. 2024

IEEE Trans.Inform.Forensic Secur.

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Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression

Chen,

Si,

Zhang

et al. 2024

Sensors

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Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz’s ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves ACCrobust scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model’s adversarial robustness while maintaining a high accuracy rate.

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CardioDefense: Defending against adversarial attack in ECG classification with adversarial distillation training

Cited by 3 publications

References 26 publications

Wave masked autoencoder: An electrocardiogram signal diagnosis model based on wave making strategy

Wave masked autoencoder: An electrocardiogram signal diagnosis model based on wave making strategy

Learn to Defend: Adversarial Multi-Distillation for Automatic Modulation Recognition Models

Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression

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