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Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains.
Heinrich von Stackelberg’s Marktform und Gleichgewicht
F.M. Scherer · 1996
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Adversarial Classification
Nilesh Dalvi, Pedro Domingos, Mausam, Sumit Sanghai, and Deepak Verma · 2004
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
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Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Network in Network
Min Lin, Qiang Chen, and Shuicheng Yan · 2014
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Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
A. Nguyen, J. Yosinski, and J. Clune · 2014
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Sequence to Sequence Learning with Neural Networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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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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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks
Chunshui Cao, Xianming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, Deva Ramanan, and Thomas S Huang · 2015
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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
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Deep learning
Yann Lecun, Yoshua Bengio, and Geoffrey Hinton · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
O. Ronneberger, P.Fischer, and T. Brox · 2015
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Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Visualizing and Understanding Recurrent Networks
A. Karpathy, J. Johnson, and L. Fei-Fei · 2016
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Transferability in Machine Learning: from Phenomena to Black-box Attacks Using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 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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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Mastering the Game of Go with Deep Neural Networks and Tree Search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Learning Deep Features for Discriminative Localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David A. Wagner · 2017
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Real Time Image Saliency for Black Box Classifiers
P. Dabkowski and Y. Gal · 2017
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Sanity Checks for Saliency Maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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Towards Better Understanding of Gradient-based Attribution Methods for Deep Neural Networks
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2018
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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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Towards Explanation of DNN-based Prediction with Guided Feature Inversion
Mengnan Du, Ninghao Liu, Qingquan Song, and Xia Hu · 2018
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Towards Explanation of DNN-based Prediction with Guided Feature Inversion
Mengnan Du, Ninghao Liu, Qingquan Song, and Xia Hu · 2018
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AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation
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Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong
Warren He, James Wei, Xinyun Chen, Nicholas Carlini, and Dawn Song · 2017
Cited alongside, same era.
Densely Connected Convolutional Networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
The (Un)reliability of Saliency Methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2017
Cited alongside, same era.
Adversarial Machine Learning at Scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2017
Cited alongside, same era.
T. Gehr, M. Mirman, D. Drachsler-Cohen, P. Tsankov, S. Chaudhuri, and M. Vechev · 2018
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Skin lesion diagnosis using ensembles, unscaled multi-crop evaluation and loss weighting
Nils Gessert, Thilo Sentker, Frederic Madesta, Rüdiger Schmitz, Helge Kniep, Ivo M. Baltruschat, René Werner, and Alexander Schlaefer · 2018
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LEMNA: Explaining Deep Learning Based Security Applications
Wenbo Guo, Dongliang Mu, Jun Xu, Purui Su, Gang Wang, and Xinyu Xing · 2018
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Adversarial Detection with Model Interpretation
Ninghao Liu, Hongxia Yang, and Xia Hu · 2018
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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E. Houle, and James Bailey · 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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Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
W. J. Murdoch, P. J. Liu, and B. Yu · 2018
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Attacks Meet Interpretability: Attribute-Steered Detection of Adversarial Samples
Guanhong Tao, Shiqing Ma, Yingqi Liu, and Xiangyu Zhang · 2018
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Ensemble Adversarial Training: Attacks and Defenses
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel · 2018
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Spatially Transformed Adversarial Examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
W. Xu, D. Evans, and Y. Qi · 2018
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Interpretable Convolutional Neural Networks
Q. Zhang, Y. Nian Wu, and S.-C. Zhu · 2018
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Interpretable Convolutional Neural Networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
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ADef: an Iterative Algorithm to Construct Adversarial Deformations
Rima Alaifari, Giovanni S. Alberti, and Tandri Gauksson · 2019
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DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model
X. Ling, S. Ji, J. Zou, J. Wang, C. Wu, B. Li, and T. Wang · 2019
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Robustness May Be at Odds with Accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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