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Prevailing defense mechanisms against adversarial face images tend to overfit to the adversarial perturbations in the training set and fail to generalize to unseen adversarial attacks.
The mnist database of handwritten digits
Yann LeCun · 1998
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From few to many: Illumination cone models for face recognition under variable lighting and pose
Athinodoros S. Georghiades, Peter N. Belhumeur, and David J. Kriegman · 2001
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Multi-PIE
Ralph Gross, Iain Matthews, Jeffrey Cohn, Takeo Kanade, and Simon Baker · 2010
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Police arrest passenger who boarded plane in Hong Kong as an old man in flat cap and arrived in Canada a young Asian refugee
Daily Mail · 2011
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NIST special database 32-multiple encounter dataset II (MEDS-II)
Andrew P. Founds, Nick Orlans, Whiddon Genevieve, and Craig I. Watson · 2011
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The challenge of face recognition from digital point-and-shoot cameras
J. Ross Beveridge, P. Jonathon Phillips, David S. Bolme, Bruce A. Draper, Geof H. Givens, Yui Man Lui, Mohammad Nayeem Teli, Hao Zhang, W. Todd Scruggs, Kevin W. Bowyer, Patrick J. Flynn, and Su Cheng · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Learning face representation from scratch
Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Early methods for detecting adversarial images
Dan Hendrycks and Kevin Gimpel · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Joint face detection and alignment using multitask cascaded convolutional networks
Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, and Yu Qiao · 2016
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Adversarial and clean data are not twins
Zhitao Gong, Wenlu Wang, and Wei-Shinn Ku · 2017
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From few to many: Illumination cone models for face recognition under variable lighting and pose
Moosavi-Dezfooli, Seyed-Mohsen, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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Detecting adversarial samples from artifacts
Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner · 2017
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On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel · 2017
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Adversarial examples detection in deep networks with convolutional filter statistics
Xin Li and Fuxin Li · 2017
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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens Van Der Maaten · 2017
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On detecting adversarial perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff · 2017
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Logit pairing methods can fool gradient-based attacks
Marius Mosbach, Maksym Andriushchenko, Thomas Trost, Matthias Hein, and Dietrich Klakow · 2018
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Generating adversarial examples with adversarial networks
Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song · 2018
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Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
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Efficient decision-based black-box adversarial attacks on face recognition
Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu · 2019
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Fast geometrically-perturbed adversarial faces
Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, and Nasser Nasrabadi · 2019
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Cascade adversarial machine learning regularized with a unified embedding
Taesik Na, Jong Hwan Ko, and Saibal Mukhopadhyay · 2017
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Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2017
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Efficient defenses against adversarial attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Magnet and “efficient defenses against adversarial attacks” are not robust to adversarial examples
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, and Bo Li · 2019
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Protecting world leaders against deep fakes
Shruti Agarwal, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, and Hao Li · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Rob-gan: Generator, discriminator, and adversarial attacker
Xuanqing Liu and Cho-Jui Hsieh · 2019
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
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Adversarial defense via learning to generate diverse attacks
Yunseok Jang, Tianchen Zhao, Seunghoon Hong, and Honglak Lee · 2019
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Detecting and mitigating adversarial perturbations for robust face recognition
Gaurav Goswami, Akshay Agarwal, Nalini Ratha, Richa Singh, and Mayank Vatsa · 2019
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Detection based defense against adversarial examples from the steganalysis point of view
Jiayang Liu, Weiming Zhang, Yiwei Zhang, Dongdong Hou, Yujia Liu, Hongyue Zha, and Nenghai Yu · 2019
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Feature distillation: Dnn-oriented jpeg compression against adversarial examples
Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen · 2019
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Adversarial training can hurt generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C Duchi, and Percy Liang · 2019
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Diversity-sensitive conditional generative adversarial networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, and Honglak Lee · 2019
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Advfaces: Adversarial face synthesis
Debayan Deb, Jianbang Zhang, and Anil K Jain · 2020
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On disentangling spoof traces for generic face anti-spoofing
Yaojie Liu, Joel Stehouwer, and Xiaoming Liu · 2020
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On the detection of digital face manipulation
Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, and Anil Jain · 2020
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Fawkes: protecting privacy against unauthorized deep learning models
Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y Zhao · 2020
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Detection of face recognition adversarial attacks
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Image transformation based defense against adversarial perturbation on deep learning models
Akshay Agarwal, Richa Singh, Mayank Vatsa, and Nalini K Ratha · 2020
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A self-supervised approach for adversarial robustness
Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli · 2020
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Manifold projection for adversarial defense on face recognition
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