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Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications.
Seeing isn’t believing: Towards more robust adversarial attack against real world object detectors
Zhao, Y.; Zhu, H.; Liang, R.; Shen, Q.; Zhang, S.; and Chen, K. 2019 · 2004
Earlier work this paper cites.
Delving into the adversarial robustness on face recognition
Yang, X.; Yang, D.; Dong, Y.; Yu, W.; Su, H.; and Zhu, J. 2020 · 2007
Earlier work this paper cites.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Huang, G. B.; Mattar, M.; Berg, T.; and Learned-Miller, E. 2008 · 2008
Earlier work this paper cites.
Curriculum learning
Bengio, Y.; Louradour, J.; Collobert, R.; and Weston, J. 2009 · 2009
Earlier work this paper cites.
Self-Paced Learning for Latent Variable Models
Kumar, M. P.; Packer, B.; and Koller, D. 2010 · 2010
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
Earlier work this paper cites.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Sharif, M.; Bhagavatula, S.; Bauer, L.; and Reiter, M. K. 2016 · 2016
Earlier work this paper cites.
Self-paced learning: an implicit regularization perspective
Fan, Y.; He, R.; Liang, J.; and Hu, B. 2017 · 2017
Earlier work this paper cites.
Synthesizing robust adversarial examples
Athalye, A.; Engstrom, L.; Ilyas, A.; and Kwok, K. 2018 · 2018
Earlier work this paper cites.
Curriculum adversarial training
Cai, Q.-Z.; Du, M.; Liu, C.; and Song, D. 2018 · 2018
Earlier work this paper cites.
Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector
Chen, S.-T.; Cornelius, C.; Martin, J.; and Chau, D. H. P. 2018 · 2018
Cited alongside, same era.
Robust physical-world attacks on deep learning visual classification
Eykholt, K.; Evtimov, I.; Fernandes, E.; Li, B.; Rahmati, A.; Xiao, C.; Prakash, A.; Kohno, T.; and Song, D. 2018 · 2018
Cited alongside, same era.
Cosface: Large margin cosine loss for deep face recognition
Wang, H.; Wang, Y.; Zhou, Z.; Ji, X.; Gong, D.; Zhou, J.; Li, Z.; and Liu, W. 2018 · 2018
Cited alongside, same era.
CAMOU: Learning physical vehicle camouflages to adversarially attack detectors in the wild
Zhang, Y.; Foroosh, H.; David, P.; and Gong, B. 2018 · 2018
Cited alongside, same era.
Arcface: Additive angular margin loss for deep face recognition
Deng, J.; Guo, J.; Xue, N.; and Zafeiriou, S. 2019 · 2019
Cited alongside, same era.
advPattern: physical-world attacks on deep person re-identification via adversarially transformable patterns
Wang, Z.; Zheng, S.; Song, M.; Wang, Q.; Rahimpour, A.; and Qi, H. 2019 · 2019
Later among the works it cites.
Adversarial camouflage: Hiding physical-world attacks with natural styles
Duan, R.; Ma, X.; Wang, Y.; Bailey, J.; Qin, A. K.; and Yang, Y. 2020 · 2020
Later among the works it cites.
Advhat: Real-world adversarial attack on arcface face id system
Komkov, S.; and Petiushko, A. 2020 · 2020
Later among the works it cites.
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing
Qiu, H.; Xiao, C.; Yang, L.; Yan, X.; Lee, H.; and Li, B. 2020 · 2020
Later among the works it cites.
Adversarial t-shirt! evading person detectors in a physical world
Xu, K.; Zhang, G.; Liu, S.; Fan, Q.; Sun, M.; Chen, H.; Chen, P.-Y.; Wang, Y.; and Lin, X. 2020 · 2020
Later among the works it cites.
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Dong, Y.; Su, H.; Wu, B.; Li, Z.; Liu, W.; Zhang, T.; and Zhu, J. 2019 · 2019
Cited alongside, same era.
Connecting the digital and physical world: Improving the robustness of adversarial attacks
Jan, S. T.; Messou, J.; Lin, Y.-C.; Huang, J.-B.; and Wang, G. 2019 · 2019
Cited alongside, same era.
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Li, J.; Schmidt, F.; and Kolter, Z. 2019 · 2019
Cited alongside, same era.
On adversarial patches: real-world attack on arcface-100 face recognition system
Pautov, M.; Melnikov, G.; Kaziakhmedov, E.; Kireev, K.; and Petiushko, A. 2019 · 2019
Cited alongside, same era.
A general framework for adversarial examples with objectives
Sharif, M.; Bhagavatula, S.; Bauer, L.; and Reiter, M. K. 2019 · 2019
Cited alongside, same era.
Universal physical camouflage attacks on object detectors
Huang, L.; Gao, C.; Zhou, Y.; Xie, C.; Yuille, A. L.; Zou, C.; and Liu, N. 2020a
Cited in the paper.
Curricularface: adaptive curriculum learning loss for deep face recognition
Huang, Y.; Wang, Y.; Tai, Y.; Liu, X.; Shen, P.; Li, S.; Li, J.; and Huang, F. 2020b
Cited in the paper.
Defenses Against Multi-sticker Physical Domain Attacks on Classifiers
Zhao, X.; and Stamm, M. C. 2020 · 2020
Later among the works it cites.
Improving Transferability of Adversarial Patches on Face Recognition With Generative Models
Xiao, Z.; Gao, X.; Fu, C.; Dong, Y.; Gao, W.; Zhang, X.; Zhou, J.; and Zhu, J. 2021 · 2021
Closest in time.
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
Yin, B.; Wang, W.; Yao, T.; Guo, J.; Kong, Z.; Ding, S.; Li, J.; and Liu, C. 2021 · 2021
Closest in time.
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Zolfi, A.; Kravchik, M.; Elovici, Y.; and Shabtai, A. 2021 · 2021
Closest in time.