ArticleBioengineering (Basel, Switzerland)2023
The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The ASC Module: A GPU Memory-Efficient, Physiology-Aware Approach for Improving Segmentation Accuracy on Poorly Contrast-Enhanced CT Scans-A Preliminary Study.Bioengineering (Basel, Switzerland) · 2025Article
- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.Medical image learning with limited and noisy data : second international workshop, MILLanD 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MILLanD (Workshop) : (2nd : 2023 : Vancouver, B... · 2023Article
- Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
The coronavirus disease 2019 (COVID-19) rapidly spread around the world, and resulted in a global pandemic. Applying artificial intelligence to COVID-19 research can produce very exciting results. However, most research has focused on applying AI techniques in the study of COVID-19, but has ignored the security and reliability of AI systems. In this paper, we explore adversarial attacks on a deep learning system based on COVID-19 CT images with the aim of helping to address this problem. Firstly, we built a deep learning system that could identify COVID-19 CT images and non-COVID-19 CT images with an average accuracy of 76.27%. Secondly, we attacked the pretrained model with an adversarial attack algorithm, i.e., FGSM, to cause the COVID-19 deep learning system to misclassify the CT images, and the classification accuracy of non-COVID-19 CT images dropped from 80% to 0%. Finally, in response to this attack, we proposed how a more secure and reliable deep learning model based on COVID-19 medical images could be built. This research is based on a COVID-19 CT image recognition system, which studies the security of a COVID-19 CT image-based deep learning system. We hope to draw more researchers' attention to the security and reliability of medical deep learning systems.
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