RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on face recognition

Author:

Liu Xiaoliang,Shen FuraoORCID,Zhao JianORCID,Nie Changhai

Publisher

Elsevier BV

Reference40 articles.

1. Explaining and harnessing adversarial examples;Goodfellow,2014

2. Towards deep learning models resistant to adversarial attacks;Madry,2018

3. M. Sharif, S. Bhagavatula, L. Bauer, M.K. Reiter, Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition, in: ACM SIGSAC Conference on Computer and Communications Security, 2016, pp. 1528–1540.

4. Robust physical-world attacks on face recognition;Zheng;Pattern Recognit.,2023

5. RSTAM: An effective black-box impersonation attack on face recognition using a mobile and compact printer;Liu,2022

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