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Images perturbed subtly to be misclassified by neural networks, called adversarial examples, have emerged as a technically deep challenge and an important concern for several application domains.
An analysis of approximations for maximizing submodular set functions
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher · 1978
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Updating quasi-newton matrices with limited storage
Jorge Nocedal · 1980
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Texture synthesis by non-parametric sampling
Alexei A Efros and Thomas K Leung · 1999
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Polynomial texture maps
Tom Malzbender, Dan Gelb, and Hans Wolters · 2001
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k-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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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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Compositional pattern producing networks: A novel abstraction of development
K O Stanley · 2007
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Facial recognition technology: A survey of policy and implementation issues
Lucas Introna and Helen Nissenbaum · 2009
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Attribute and simile classifiers for face verification
Neeraj Kumar, Alexander C Berg, Peter N Belhumeur, and Shree K Nayar · 2009
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CV Dazzle: Camouflage from face detection
Adam Harvey · 2010
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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The Chi-square test of independence
Mary L McHugh · 2013
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Privacy Visor: Method based on light absorbing and reflecting properties for preventing face image detection
Takayuki Yamada, Seiichi Gohshi, and Isao Echizen · 2013
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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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Practical evasion of a learning-based classifier: A case study
Nedim Srndic and Pavel Laskov · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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DeepFace: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Deep face recognition
Omkar M. Parkhi, Andrea Vedaldi, and Andrew Zisserman · 2015
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nlme: Linear and Nonlinear Mixed Effects Models , 2015
Jose Pinheiro, Douglas Bates, Saikat DebRoy, Deepayan Sarkar, and R Core Team · 2015
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MatConvNet – convolutional neural networks for MATLAB
A. Vedaldi and K. Lenc · 2015
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OpenFace: A general-purpose face recognition library with mobile applications
Brandon Amos, Bartosz Ludwiczuk, and Mahadev Satyanarayanan · 2016
Cited alongside, same era.
Openface: An open source facial behavior analysis toolkit
Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency · 2016
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Hidden voice commands
Nicholas Carlini, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah Sherr, Clay Shields, David Wagner, and Wenchao Zhou · 2016
Cited alongside, same era.
Robustness of classifiers: From adversarial to random noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2016
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Evasion and hardening of tree ensemble classifiers
Alex Kantchelian, JD Tygar, and Anthony D. Joseph · 2016
Cited alongside, same era.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Simple black-box adversarial attacks on deep neural networks
Nina Narodytska and Shiva Kasiviswanathan · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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JetBlue ditching boarding passes for facial recognition
Yaron Steinbuch · 2017
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DolphinAttack: Inaudible voice commands
Guoming Zhang, Chen Yan, Xiaoyu Ji, Taimin Zhang, Tianchen Zhang, and Wenyuan Xu · 2017
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On the robustness of the CVPR 2018 white-box adversarial example defenses
Anish Athalye and Nicholas Carlini · 2018
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Distributional smoothing with virtual adversarial training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2016
Cited alongside, same era.
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
Cited alongside, same era.
Adversarial diversity and hard positive generation
Andras Rozsa, Ethan M. Rudd, and Terrance E. Boult · 2016
Cited alongside, same era.
Adversarial manipulation of deep representations
Sara Sabour, Yanshuai Cao, Fartash Faghri, and David J. Fleet · 2016
Cited alongside, same era.
Closest in time.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Learning to attack: Adversarial transformation networks
Shumeet Baluja and Ian Fischer · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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Robust physical-world attacks on machine learning models
Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song · 2018
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Motivating the rules of the game for adversarial example research
Justin Gilmer, Ryan P Adams, Ian Goodfellow, David Andersen, and George E Dahl · 2018
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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2018
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Geometric robustness of deep networks: analysis and improvement
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
J Zico Kolter and Eric Wong · 2018
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Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jun Zhu, and Xiaolin Hu · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev · 2018
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Generative adversarial perturbations
Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge Belongie · 2018
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Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima · 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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Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh · 2018
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A simple explanation for the existence of adversarial examples with small hamming distance
Adi Shamir, Itay Safran, Eyal Ronen, and Orr Dunkelman · 2019
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