Title: Increasing-Margin Adversarial (IMA) training to Improve Adversarial Robustness of Neural Networks. Abstract: Deep neural networks (DNNs), including convolutional neural networks, are known to be vulnerable to adversarial attacks, which may lead to disastrous consequences in life-critical applications. Adversarial samples are usually generated by attack algorithms and can also be induced by white noises, and therefore the threats are real. In this study, we propose a novel training method, named Increasing Margin Adversarial (IMA) Training, to improve DNN robustness against adversarial noises. During training, the IMA method increases the margins of training samples by moving the decision boundaries of the DNN model far away from the training samples to improve robustness. The IMA method is evaluated on six publicly available datasets (including a COVID-19 CT image dataset) under strong 100-PGD white-box adversarial attacks, and the results show that the proposed method significantly improved classification accuracy on noisy data while keeping a relatively high accuracy on clean data. We hope our approach may facilitate the development of robust DNN applications, especially for COVID-19 diagnosis using CT images.