Abstract
The Coronavirus disease 2019 (COVID-19) is a pandemic that occurred in December 2019 and spread globally. Most of the current research is on how to apply deep learning to detect COVID-19, but little research has been done on the security of COVID-19 deep learning systems. Therefore, we test and verify the security of COVID-19 CT images deep learning system with adversarial attack. Firstly, we build a deep learning system for recognizing COVID-19 CT images. Secondly, adding imperceptible disturbance to CT images will lead to neural network classification errors. Finally, we discuss the application of formal methods and formal verification to deep learning systems. We hope to draw more attention from researchers to the application of formal methods and formal verification to artificial intelligence.
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Acknowledgment
The work is supported partially by JST SPRING, Grant Number JPMJSP2132.
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Li, Y., Liu, S. (2023). Testing and Verifying the Security of COVID-19 CT Images Deep Learning System with Adversarial Attack. In: Liu, S., Duan, Z., Liu, A. (eds) Structured Object-Oriented Formal Language and Method. SOFL+MSVL 2022. Lecture Notes in Computer Science, vol 13854. Springer, Cham. https://doi.org/10.1007/978-3-031-29476-1_10
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DOI: https://doi.org/10.1007/978-3-031-29476-1_10
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