Fetching the paper…
Reading the bibliography…
Face recognition (FR) has recently made substantial progress and achieved high accuracy on standard benchmarks.
An investigation into the use of partial-faces for face recognition
Srinivas Gutta, Vasanth Philomin, and Miroslav Trajkovic · 2002
Earlier work this paper cites.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2007
Earlier work this paper cites.
Face recognition in unconstrained videos with matched background similarity
Lior Wolf, Tal Hassner, and Itay Maoz · 2011
Earlier work this paper cites.
Deep learning face representation by joint identification-verification
Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
Earlier work this paper cites.
Learning face representation from scratch
Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition
Guosheng Hu, Yongxin Yang, Dong Yi, Josef Kittler, William Christmas, Stan Z Li, and Timothy Hospedales · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
A study of the effect of jpg compression on adversarial images
Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel M Roy · 2016
Earlier work this paper cites.
The megaface benchmark: 1 million faces for recognition at scale
Ira Kemelmacher-Shlizerman, Steven M Seitz, Daniel Miller, and Evan Brossard · 2016
Earlier work this paper cites.
Frontal to profile face verification in the wild
Soumyadip Sengupta, Jun Cheng Chen, Carlos Castillo, Vishal M. Patel, Rama Chellappa, and David W. Jacobs · 2016
Earlier work this paper cites.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
Cited alongside, same era.
A discriminative feature learning approach for deep face recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao · 2016
Cited alongside, same era.
Joint face detection and alignment using multitask cascaded convolutional networks
Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, and Yu Qiao · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Cited alongside, same era.
Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
Later among the works it cites.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
Later among the works it cites.
Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
Later among the works it cites.
Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Sphereface: Deep hypersphere embedding for face recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song · 2017
Cited alongside, same era.
Foolbox v0. 8.0: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Adversarial generative nets: Neural network attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, and Bauer · 2017
Cited alongside, same era.
Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments
Tianyue Zheng, Weihong Deng, and Jiani Hu · 2017
Cited alongside, same era.
Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices
Sheng Chen, Yang Liu, Xiang Gao, and Zhen Han · 2018
Cited alongside, same era.
Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments
Tianyue Zheng and Weihong Deng · 2018
Later among the works it cites.
On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, and Aleksander Madry · 2019
Later among the works it cites.
Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
Later among the works it cites.
Face anti-spoofing: Model matters, so does data
Xiao Yang, Wenhan Luo, Linchao Bao, Yuan Gao, Dihong Gong, Shibao Zheng, Zhifeng Li, and Wei Liu · 2019
Later among the works it cites.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
Later among the works it cites.
Adversarial vision challenge
Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Sharada P Mohanty, Florian Laurent, Marcel Salathé, Matthias Bethge, Yaodong Yu, et al · 2020
Closest in time.
Benchmarking adversarial robustness on image classification
Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, and Jun Zhu · 2020
Closest in time.
Design and interpretation of universal adversarial patches in face detection
Xiao Yang, Fangyun Wei, Hongyang Zhang, and Jun Zhu · 2020
Closest in time.
Facesec: A fine-grained robustness evaluation framework for face recognition systems
Liang Tong, Zhengzhang Chen, Jingchao Ni, Wei Cheng, Dongjin Song, Haifeng Chen, and Yevgeniy Vorobeychik · 2021
Closest in time.
Boosting transferability of targeted adversarial examples via hierarchical generative networks
Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2021
Closest in time.