A-XAI: adversarial machine learning for trustable explainability-Reference-Cited by-同舟云学术

A-XAI: adversarial machine learning for trustable explainability

Published:2024-01-17 Issue: Volume: Page:
ISSN:2730-5953
Container-title:AI and Ethics
language:en
Short-container-title:AI Ethics

Author:

Agrawal Nishita,Pendharkar Isha,Shroff Jugal,Raghuvanshi Jatin,Neogi Akashdip,Patil Shruti,Walambe Rahee^ORCID,Kotecha Ketan

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s43681-023-00368-4.pdf

Reference27 articles.

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3. Gragnaniello, D., Marra, F., Poggi, G., Verdoliva, L.: Analysis of adversarial attacks against CNN-based image forgery detectors. In: 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, pp 967–971 (2018), https://doi.org/10.23919/EUSIPCO.2018.8553560.

4. Saleh, A., Sukaik, R., Abu-Naser, S.S.: Brain tumor classification using deep learning. In: 2020 International Conference on Assistive and Rehabilitation Technologies (iCareTech), pp. 131–136 (2020), https://doi.org/10.1109/iCareTech49914.2020.00032

5. Soomro, T.A., et al.: Image segmentation for MR brain tumor detection using machine learning: a review. IEEE Rev. Biomed. Eng. 15, 10 (2022). https://doi.org/10.1109/RBME.2022.3185292

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