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The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications.
Principal warps: Thin-plate splines and the decomposition of deformations
F. L. Bookstein · 1989
Earlier work this paper cites.
On the limited memory BFGS method for large scale optimization
D. C. Liu and J. Nocedal · 1989
Earlier work this paper cites.
3d shape-based face recognition using automatically registered facial surfaces
M. O. Irfanoglu, B. Gokberk, and L. Akarun · 2004
Earlier work this paper cites.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller · 2008
Earlier work this paper cites.
Dlib-ml: A machine learning toolkit
D. E. King · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples (2014)
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
S. Gu and L. Rigazio · 2014
Earlier work this paper cites.
Learning face representation from scratch
D. Yi, Z. Lei, S. Liao, and S. Z. Li · 2014
Earlier work this paper cites.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Earlier work this paper cites.
Deep face recognition
O. M. Parkhi, A. Vedaldi, A. Zisserman, et al · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
Cited alongside, same era.
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
Cv dazzle: Camouflage from face detection
A. Harvey · 2017
Later among the works it cites.
Detecting adversarial examples in deep networks with adaptive noise reduction
B. Liang, H. Li, M. Su, X. Li, W. Shi, and X. Wang · 2017
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Later among the works it cites.
Yolo9000: better, faster, stronger
J. Redmon and A. Farhadi · 2017
Later among the works it cites.
Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel · 2017
Later among the works it cites.
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Adversarial diversity and hard positive generation
A. Rozsa, E. M. Rudd, and T. E. Boult · 2016
Cited alongside, same era.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
Cited alongside, same era.
Learning adversary-resistant deep neural networks
Q. Wang, W. Guo, K. Zhang, I. Ororbia, G. Alexander, X. Xing, X. Liu, and C. L. Giles · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, L. Chen, M. E. Kounavis, and D. H. Chau · 2017
Cited alongside, same era.
W. Wang, A. Wang, A. Tamar, X. Chen, and P. Abbeel · 2017
Later among the works it cites.
VGGFace2: A dataset for recognising faces across pose and age
Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman · 2018
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Boosting adversarial attacks with momentum
Y. Dong, F. Liao, T. Pang, H. Su, X. Hu, J. Li, and J. Zhu · 2018
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Smartbox: Benchmarking adversarial detection and mitigation algorithms for face recognition
A. Goel, A. Singh, A. Agarwal, M. Vatsa, and R. Singh · 2018
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Defense against adversarial attacks using high-level representation guided denoiser
F. Liao, M. Liang, Y. Dong, T. Pang, J. Zhu, and X. Hu · 2018
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Spatially transformed adversarial examples
C. Xiao, J.-Y. Zhu, B. Li, W. He, M. Liu, and D. Song · 2018
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