Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients

26 November 2017

Finale Doshi-Velez

Papers citing "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

29 / 29 papers shown

Title
MIRACLE3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model Construction Hossein Resani B. Nasihatkon 3DV 430 0 0 08 Oct 2024
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective Tal Alter Raz Lapid Moshe Sipper AAML 100 6 0 25 Aug 2024
Robust Explainable Recommendation Sairamvinay Vijayaraghavan Prasant Mohapatra AAML 67 0 0 03 May 2024
Towards Adversarially Robust Dataset Distillation by Curvature Regularization Eric Xue Yijiang Li Haoyang Liu Yifan Shen Haohan Wang Haohan Wang DD 117 8 0 15 Mar 2024
Specification Overfitting in Artificial Intelligence Benjamin Roth Pedro Henrique Luz de Araujo Yuxi Xia Saskia Kaltenbrunner Christoph Korab 196 1 0 13 Mar 2024
Set-Based Training for Neural Network Verification Lukas Koller Tobias Ladner Matthias Althoff AAML 90 2 0 26 Jan 2024
On Continuity of Robust and Accurate Classifiers Ramin Barati Reza Safabakhsh Mohammad Rahmati AAML 76 1 0 29 Sep 2023
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 207 2,235 0 12 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,729 0 19 May 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 92 558 0 11 Apr 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 89 1,271 0 04 Apr 2017
Improved Training of Wasserstein GANs Ishaan Gulrajani Faruk Ahmed Martín Arjovsky Vincent Dumoulin Aaron Courville GAN 227 9,560 0 31 Mar 2017
Biologically inspired protection of deep networks from adversarial attacks Aran Nayebi Surya Ganguli AAML 71 115 0 27 Mar 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 133 591 0 10 Mar 2017
Gradients of Counterfactuals Mukund Sundararajan Ankur Taly Qiqi Yan FAtt 73 104 0 08 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 472 3,148 0 04 Nov 2016
Defensive Distillation is Not Robust to Adversarial Examples Nicholas Carlini D. Wagner 67 339 0 14 Jul 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 547 5,910 0 08 Jul 2016
Auditing Black-box Models for Indirect Influence Philip Adler Casey Falk Sorelle A. Friedler Gabriel Rybeck C. Scheidegger Brandon Smith Suresh Venkatasubramanian TDI MLAU 166 290 0 23 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,033 0 16 Feb 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 75 3,682 0 08 Feb 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 115 3,967 0 24 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 113 3,076 0 14 Nov 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,364 0 22 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,121 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 284 14,963 1 21 Dec 2013
Do Deep Nets Really Need to be Deep? Lei Jimmy Ba R. Caruana 173 2,119 0 21 Dec 2013
Convolutional Neural Networks Applied to House Numbers Digit Classification P. Sermanet Soumith Chintala Yann LeCun 99 543 0 18 Apr 2012
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 140 1,104 0 06 Dec 2009