关键词: 新冠肺炎CT影像, 注意力引导机制, 深度学习, DNN对抗攻击

Abstract: In the research of applying deep learning to intelligent COVID-19 CT recognition, a large number of researchers train DNN models to understand the content of medical image, and assist in the diagnosis of the COVID-19. Firstly, this paper proposes the AMDRC-Net architecture, in which the residual structure solves the network degradation problem through identity mapping. However, the residual structure hinders the exploration of new features, and the long and short attention guidance mechanism is inspired by the latest research such as the attention mechanism. Afterwards, it focuses on the security of deep learning models and discusses the adversarial attack based on gradient ascent. In order to solve the problem of singularity, the long and short attention mechanism is used to increase effective counter disturbances while reducing redundant disturbances. Then, the counterattack attacks proposed algorithm A-IM-FGSM transforms the adversarial attack problem into an adaptive constraint problem, that is, the idea of micro-transformation can be used in iterative attacks to explore the relationship between the attention guidance mechanism and DNN adversarial attack. In the final experiments, the AMDRC-Net is used for model training on the COVID-19 CT dataset, the comparison experiments, visualization experiments, and adversarial attack experiments are completed.

Key words: COVID-19 CT image, attention mechanism, deep learning, DNN adversarial attacks