ECG-ATK-GAN: Robustness against Adversarial Attacks on ECG using Conditional Generative Adversarial Networks

Recently deep learning has reached human-level performance in classifying arrhythmia from Electrocardiogram (ECG). However, deep neural networks (DNN) are vulnerable to adversarial attacks, which can misclassify ECG signals by decreasing the model's precision. Adversarial attacks are crafted perturbations injected in data that manifest the conventional DNN models to misclassify the correct class. Thus, safety concerns arise as it becomes challenging to establish the system's reliability, given that clinical applications require high levels of trust. To mitigate this problem and make DNN models more robust in clinical and real-life settings, we introduce a novel Conditional Generative Adversarial Network (GAN), robust against adversarial attacked ECG signals and retaining high accuracy. Furthermore, we compared it with other state-of-art models to detect cardiac abnormalities from indistinguishable adversarial attacked ECGs. The experiment confirms, our model is more robust against adversarial attacks compared to other architectures.