Exploiting the Sensitivity of $L_2$ Adversarial Examples to
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Exploiting the Sensitivity of $L_2$ Adversarial Examples to Erase-and-Restore

1 January 2020

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Papers citing "Exploiting the Sensitivity of $L_2$ Adversarial Examples to Erase-and-Restore"

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Title
Adversarial Robustness Toolbox v1.0.0 Maria-Irina Nicolae M. Sinn Minh-Ngoc Tran Beat Buesser Ambrish Rawat ... Nathalie Baracaldo Bryant Chen Heiko Ludwig Ian Molloy Ben Edwards AAML VLM 79 460 0 03 Jul 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 243 3,194 0 01 Feb 2018
Deflecting Adversarial Attacks with Pixel Deflection Aaditya (Adi) Prakash N. Moran Solomon Garber Antonella DiLillo J. Storer AAML 81 303 0 26 Jan 2018
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser Fangzhou Liao Ming Liang Yinpeng Dong Tianyu Pang Xiaolin Hu Jun Zhu 87 888 0 08 Dec 2017
Countering Adversarial Images using Input Transformations Chuan Guo Mayank Rana Moustapha Cissé Laurens van der Maaten AAML 128 1,406 0 31 Oct 2017
Foolbox: A Python toolbox to benchmark the robustness of machine learning models Jonas Rauber Wieland Brendel Matthias Bethge AAML 80 283 0 13 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 319 12,138 0 19 Jun 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini Basel Alomair AAML 102 242 0 15 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 131 1,867 0 20 May 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 97 1,273 0 04 Apr 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 86 714 0 21 Feb 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 73 950 0 14 Feb 2017
An empirical study on the effects of different types of noise in image classification tasks G. B. P. D. Costa Welinton A. Contato T. S. Nazaré J. B. Neto M. Ponti 60 40 0 09 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 154 4,905 0 14 Nov 2015

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