Understanding and Enhancing the Transferability of Adversarial Examples

27 February 2018

Papers citing "Understanding and Enhancing the Transferability of Adversarial Examples"

23 / 23 papers shown

Title
Sensitivity and Generalization in Neural Networks: an Empirical Study Roman Novak Yasaman Bahri Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein AAML 93 439 0 23 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 185 3,180 0 01 Feb 2018
Spatially Transformed Adversarial Examples Chaowei Xiao Jun-Yan Zhu Yue Liu Warren He M. Liu D. Song AAML 70 522 0 08 Jan 2018
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel Jonas Rauber Matthias Bethge AAML 65 1,342 0 12 Dec 2017
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 147 680 0 26 Nov 2017
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples Yinpeng Dong Hang Su Jun Zhu Fan Bao AAML 116 129 0 18 Aug 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 71 1,875 0 14 Aug 2017
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles Jiajun Lu Hussein Sibai Evan Fabry David A. Forsyth AAML 78 280 0 12 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 265 12,029 0 19 Jun 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 199 2,221 0 12 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,720 0 19 May 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 79 556 0 11 Apr 2017
The Shattered Gradients Problem: If resnets are the answer, then what is the question? David Balduzzi Marcus Frean Lennox Leary J. P. Lewis Kurt Wan-Duo Ma Brian McWilliams ODL 68 401 0 28 Feb 2017
Towards the Science of Security and Privacy in Machine Learning Nicolas Papernot Patrick McDaniel Arunesh Sinha Michael P. Wellman AAML 74 473 0 11 Nov 2016
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 133 1,731 0 08 Nov 2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 94 773 0 06 Nov 2016
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 127 2,525 0 26 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 222 8,533 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 517 5,885 0 08 Jul 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 88 1,738 0 24 May 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 64 3,672 0 08 Feb 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 229 19,017 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 235 14,893 1 21 Dec 2013