Early detection of COVID-19 allows medical intervention to improve the survival rate of patients. The use of deep neural networks （DNN） to detect COVID-19 can improve the sensitivity and speed of interpretation of chest CT for COVID-19 screening. However， applying DNN for the medical field is known to be influenced by the limited samples and imperceptible noise perturbations. In this paper， we propose a multi-loss hybrid adversarial function （MLAdv） to search the effective adversarial attack samples containing potential spoofing networks. These adversarial attack samples are then added to the training data to improve the robustness and the generalization of the network for unanticipated noise perturbations. Especially， MLAdv not only implements the multiple-loss function including style， origin， and detail losses to craft medical adversarial samples into realistic-looking styles， but also uses the heuristic projection algorithm to produce the noise with strong aggregation and interference. These samples are proven to have stronger anti-noise ability and attack transferability. By evaluating on COVID-19 dataset， it is shown that the augmented networks by using adversarial attacks from the MLAdv algorithm can improve the diagnosis accuracy by 4.75%. Therefore， the augmented network based on MLAdv adversarial attacks can improve the ability of models and is resistant to noise perturbations.