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“Problems of monetary management: the UK experience”
Charles Goodhart · 1984
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“An analysis of the 1999 DARPA/Lincoln Laboratory evaluation data for network anomaly detection”
Matthew Mahoney and Philip Chan · 2003
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“Adversarial classification”
Nilesh Dalvi, Pedro Domingos, Sumit Sanghai and Deepak Verma · 2004
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“Anagram: A content anomaly detector resistant to mimicry attack”
Ke Wang, Janak Parekh and Salvatore Stolfo · 2006
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“Nightmare at test time: robust learning by feature deletion”
Amir Globerson and Sam Roweis · 2006
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“Spam Filtering Using Inexact String Matching in Explicit Feature Space with On-Line Linear Classifiers.”
D Sculley, Gabriel Wachman and Carla Brodley · 2006
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“Open problems in the security of learning”
Marco Barreno, Peter Bartlett, Fuching Chi, Anthony Joseph, Blaine Nelson, Benjamin Rubinstein, Udam Saini and J Tygar · 2008
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“Mean squared error: Love it or leave it? A new look at signal fidelity measures”
Z. Wang and A.. Bovik · 2009
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“The security of machine learning”
Marco Barreno, Blaine Nelson, Anthony Joseph and JD Tygar · 2010
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“Adversarial machine learning”
Ling Huang, Anthony Joseph, Blaine Nelson, Benjamin Rubinstein and JD Tygar · 2011
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“A Machine Learning Approach to Android Malware Detection”
Justin Sahs and Latifur Khan · 2012
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“Large-scale malware classification using random projections and neural networks”
George. Dahl, Jack. Stokes, Li Deng and Dong Yu · 2013
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“Multi-digit number recognition from street view imagery using deep convolutional neural networks”
Ian Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud and Vinay Shet · 2013
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“Intriguing properties of neural networks”
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow and Rob Fergus · 2014
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“Towards deep neural network architectures robust to adversarial examples”
Shixiang Gu and Luca Rigazio · 2014
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“Security evaluation of pattern classifiers under attack”
Battista Biggio, Giorgio Fumera and Fabio Roli · 2014
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“Stop?” Accessed: 2018-7-18, 2014
Flickr user “faungg” · 2014
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“Foveation-based mechanisms alleviate adversarial examples”
Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio and Qi Zhao · 2015
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“Explaining and Harnessing Adversarial Examples”
Ian. Goodfellow, Jonathon Shlens and Christian Szegedy · 2015
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“Deep neural networks are easily fooled: High confidence predictions for unrecognizable images”
Anh Nguyen, Jason Yosinski and Jeff Clune · 2015
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“Adversarial examples in the physical world”
Alexey Kurakin, Ian Goodfellow and Samy Bengio · 2016
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“The limitations of deep learning in adversarial settings”
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Celik and Ananthram Swami · 2016
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“Distillation as a defense to adversarial perturbations against deep neural networks”
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha and Ananthram Swami · 2016
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“Measuring neural net robustness with constraints”
Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori and Antonio Criminisi · 2016
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“Adversarial examples detection in deep networks with convolutional filter statistics”
Xin Li and Fuxin Li · 2016
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“Suppressing the unusual: towards robust cnns using symmetric activation functions”
Qiyang Zhao and Lewis Griffin · 2016
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“A study of the effect of JPG compression on adversarial images”
Gintare Dziugaite, Zoubin Ghahramani and Daniel Roy · 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
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“Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition”
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer and Michael Reiter · 2016
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“Automatically evading classifiers”
Weilin Xu, Yanjun Qi and David Evans · 2016
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“Adversarial perturbations against deep neural networks for malware classification”
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes and Patrick McDaniel · 2016
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“Robustness of classifiers: from adversarial to random noise”
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli and Pascal Frossard · 2016
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“Attacking Machine Learning with Adversarial Examples”
Ian Goodfellow, Nicolas Papernot, Sandy Huang, Yan Duan and Peter Abbeel · 2017
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“Towards deep learning models resistant to adversarial examples”
Aleksander Madry, Aleksander Makelov, Ludwig Schmidt, Dimitris Tsipras and Adrian Vladu · 2017
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“On detecting adversarial perturbations”
Jan Metzen, Tim Genewein, Volker Fischer and Bastian Bischoff · 2017
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“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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“Certifiable Distributional Robustness with Principled Adversarial Training”
Aman Sinha, Hongseok Namkoong and John Duchi · 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 examples in deep networks with adaptive noise reduction”
Bin Liang, Hongcheng Li, Miaoqiang Su, Xirong Li, Wenchang Shi and Xiaofeng Wang · 2017
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“When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time”
David Miller, Yulia Wang and George Kesidis · 2017
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“LatentPoison - Adversarial Attacks On The Latent Space”
Antonia Creswell, Anil. Bharath and Biswa Sengupta · 2017
Cited alongside, same era.
“Robust Physical-World Attacks on Deep Learning Models”
Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati and Dawn Song · 2017
Cited alongside, same era.
“Early Methods for Detecting Adversarial Images”
Dan Hendrycks and Kevin Gimpel · 2017
Cited alongside, same era.
“Biologically inspired protection of deep networks from adversarial attacks”
Aran Nayebi and Surya Ganguli · 2017
Cited alongside, same era.
“Safetynet: Detecting and rejecting adversarial examples robustly”
Jiajun Lu, Theerasit Issaranon and David Forsyth · 2017
Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas and Yoshua Bengio · 2018
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“Defending against Adversarial Images using Basis Functions Transformations”
Uri Shaham, James Garritano, Yutaro Yamada, Ethan Weinberger, Alex Cloninger, Xiuyuan Cheng, Kelly Stanton and Yuval Kluger · 2018
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“Combating Adversarial Attacks Using Sparse Representations”
Soorya Gopalakrishnan, Zhinus Marzi, Upamanyu Madhow and Ramtin Pedarsani · 2018
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“Robustness of Rotation-Equivariant Networks to Adversarial Perturbations”
Beranger Dumont, Simona Maggio and Pablo Montalvo · 2018
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Cited alongside, same era.
“Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks”
Weilin Xu, David Evans and Yanjun Qi · 2017
Cited alongside, same era.
“Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers”
Arjun Bhagoji, Daniel Cullina and Prateek Mittal · 2017
Cited alongside, same era.
“DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples”
Ji Gao, Beilun Wang, Zeming Lin, Weilin Xu and Yanjun Qi · 2017
Cited alongside, same era.
“Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression”
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael Kounavis and Duen Chau · 2017
Cited alongside, same era.
“Securing Deep Neural Nets against Adversarial Attacks with Moving Target Defense”
Sailik Sengupta, Tathagata Chakraborti and Subbarao Kambhampati · 2017
Cited alongside, same era.
“Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN”
Hyeungill Lee, Sungyeob Han and Jungwoo Lee · 2017
Cited alongside, same era.
“A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks”
Chang Song, Hsin-Pai Cheng, Chunpeng Wu, Hai Li, Yiran Chen and Qing Wu · 2017
Cited alongside, same era.
Sidney Pontes-Filho and Marcus Liwicki · 2018
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Yusuke Tsuzuku, Issei Sato and Masashi Sugiyama · 2018
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“Adversarially Robust Training through Structured Gradient Regularization”
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin and Thomas Hofmann · 2018
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“Improving Adversarial Robustness by Data-Specific Discretization”
Jiefeng Chen, Xi Wu, Yingyu Liang and Somesh Jha · 2018
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“Defending Against Adversarial Attacks by Leveraging an Entire GAN”
Gokula Santhanam and Paulina Grnarova · 2018
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“Scaling provable adversarial defenses”
Eric Wong, Frank Schmidt, Jan Metzen and J Kolter · 2018
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“Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders”
Partha Ghosh, Arpan Losalka and Michael Black · 2018
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“Detecting Adversarial Examples via Key-based Network”
Pinlong Zhao, Zhouyu Fu, Qinghua Hu and Jun Wang · 2018
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Yarin Gal and Lewis Smith · 2018
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“Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training”
Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen and Somesh Jha · 2018
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“Sparsity-based Defense against Adversarial Attacks on Linear Classifiers”
Zhinus Marzi, Soorya Gopalakrishnan, Upamanyu Madhow and Ramtin Pedarsani · 2018
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“Gradient Adversarial Training of Neural Networks”
Ayan Sinha, Zhao Chen, Vijay Badrinarayanan and Andrew Rabinovich · 2018
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“Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples”, 2018
Jihun Hamm · 2018
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“Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient”, 2018
Lei Wu, Zhanxing Zhu, Cheng Tai and Weinan E · 2018
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“Decision Boundary Analysis of Adversarial Examples”
Warren He, Bo Li and Dawn Song · 2018
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“Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality”
Xingjun Ma, Bo Li, Yisen Wang, Sarah Erfani, Sudanthi Wijewickrema, Michael Houle, Grant Schoenebeck, Dawn Song and James Bailey · 2018
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“Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks”, 2018
Bita Rouhani, Mohammad Samragh, Tara Javidi and Farinaz Koushanfar · 2018
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“Certifying Some Distributional Robustness with Principled Adversarial Training”
Aman Sinha, Hongseok Namkoong and John Duchi · 2018
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“Countering Adversarial Images using Input Transformations”
Chuan Guo, Mayank Rana, Moustapha Cisse and Laurens van Maaten · 2018
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“Stochastic activation pruning for robust adversarial defense”
Guneet. Dhillon, Kamyar Azizzadenesheli, Jeremy. Bernstein, Jean Kossaifi, Aran Khanna, Zachary. Lipton and Animashree Anandkumar · 2018
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“Universality, Robustness, and Detectability of Adversarial Perturbations under Adversarial Training”, 2018
Jan Metzen · 2018
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“The Manifold Assumption and Defenses Against Adversarial Perturbations”, 2018
Xi Wu, Uyeong Jang, Lingjiao Chen and Somesh Jha · 2018
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“Detecting Adversarial Perturbations with Saliency”
Chiliang Zhang, Zhimou Yang and Zuochang Ye · 2018
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“Thermometer Encoding: One Hot Way To Resist Adversarial Examples”
Jacob Buckman, Aurko Roy, Colin Raffel and Ian Goodfellow · 2018
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“Ensemble Adversarial Training: Attacks and Defenses”
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh and Patrick McDaniel · 2018
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“Adversarial examples that fool both human and computer vision”
Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow and Jascha Sohl-Dickstein · 2018
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“Audio adversarial examples: Targeted attacks on speech-to-text”
Nicholas Carlini and David Wagner · 2018
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“Adversarial Examples on Discrete Sequences for Beating Whole-Binary Malware Detection”
Felix Kreuk, Assi Barak, Shir Aviv-Reuven, Moran Baruch, Benny Pinkas and Joseph Keshet · 2018
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Anish Athalye, Nicholas Carlini and David Wagner · 2018
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“Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference”
Ruying Bao, Sihang Liang and Qingcan Wang · 2018
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“Countering Adversarial Images using Input Transformations”
Chuan Guo, Mayank Rana, Moustapha Cisse and Laurens van Maaten · 2018
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Justin Gilmer, Luke Metz, Fartash Faghri, Sam Schoenholz, Maithra Raghu, Martin Wattenberg and Ian Goodfellow · 2018
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“Empirical study of the topology and geometry of deep networks”
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard and Stefano Soatto · 2018
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“There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)”
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner and Aleksander Madry · 2018
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“Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer”
Vikas Verma, Alex Lamb, Christopher Beckham, Aaron Courville, Ioannis Mitliagkis and Yoshua Bengio · 2018
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“The unreasonable effectiveness of deep features as a perceptual metric”
Richard Zhang, Phillip Isola, Alexei Efros, Eli Shechtman and Oliver Wang · 2018
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