Adversarial robustness enhancement in deep learning-based breast cancer classification: A multi-faceted approach to poisoning and Evasion attack mitigation
Authors:
- Lourdu Mahimai Doss PLPLourdu Mahimai Doss P
- Muthumanickam GunasekaranMGMuthumanickam Gunasekaran
- Jungeun KimJKJungeun Kim
- Seifedine KadrySKSeifedine Kadry
Open Access
- 12 December 2024
- journal article
- research article
- Published by Elsevier in Alexandria Engineering Journal
- Vol. 115, 65-82
Abstract
No abstract availableKeywords
Funding Information
- Ministry of Education (2020R1I1A3069700)
- National Research Foundation of Korea
- Maryland Society of Surveyors
- Indian Institute of Technology, Patna
This publication has 54 references indexed in Scilit:
- Deceiving supervised machine learning models via adversarial data poisoning attacks: a case study with USB keyboardsInternational Journal of Information Security, 2024
- Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network ModelsInterdisciplinary Sciences: Computational Life Sciences, 2023
- Comparative evaluation of recent universal adversarial perturbations in image classificationComputers & Security, 2023
- The influence of image reconstruction methods on the diagnosis of pulmonary emphysema with convolutional neural networkRadiological Physics and Technology, 2023
- Unlocking adversarial transferability: a security threat towards deep learning-based surveillance systems via black box inference attack- a case study on face mask surveillanceMultimedia Tools and Applications, 2023
- Hybrid dilated multilayer faster RCNN for object detectionThe Visual Computer, 2023
- Adversarial security mitigations of mmWave beamforming prediction models using defensive distillation and adversarial retrainingInternational Journal of Information Security, 2022
- Technical note: Phantom‐based training framework for convolutional neural network CT noise reductionMedical Physics, 2022
- Human brain tumor classification and segmentation using CNNMultimedia Tools and Applications, 2022
- Stronger data poisoning attacks break data sanitization defensesMachine Learning, 2021