On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices

Reference : On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best...


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/53623

Title :	On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices
Language :	English
Author, co-author :	Ghamizi, Salah [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Cordy, Maxime [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
	Papadakis, Mike [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Le Traon, Yves [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
Publication date :	2022
Main document title :	The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI- 23) - SafeAI Workshop, Washington, D.C., Feb 13-14, 2023
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI- 23) - SafeAI
Event date :	07-02-2023 14-02-2023
Keywords :	[en] Chest X-ray ; Adversarial Robustness ; Evasion attacks ; CXR ; Radiograph ; NIH ; PadChest ; CheXpert
Permalink :	http://hdl.handle.net/10993/53623

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Open access	On_Evaluating_Adversarial_Robustness_of_Chest_X_ray_Classification__Requirements___Good_Practices-5.pdf		Author postprint	847.45 kB	View/Open

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