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Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors.
Adversarial Training Can Hurt Generalization
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A Geometric Perspective on the Transferability of Adversarial Directions
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Some Integrals Involving the Q_M Function
Nuttall, A. 1975 · 1975
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The Distribution Function of a Linear Combination of Chi-Squares
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Visualizing Data Using T-SNE
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On The Monotonicity of the Generalized Marcum and Nuttall Q-Functions
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
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Reading Digits in Natural Images with Unsupervised Feature Learning
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Imagenet Classification with Deep Convolutional Neural Networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A.; Toderici, G.; Shetty, S.; Leung, T.; Sukthankar, R.; and Fei-Fei, L. 2014 · 2014
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Explaining and Harnessing Adversarial Examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
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Robustness of Classifiers: from Adversarial to Random Noise
Fawzi, A.; Moosavi-Dezfooli, S.-M.; and Frossard, P. 2016 · 2016
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Delving Into Transferable Adversarial Examples and Black-Box Attacks
Liu, Y.; Chen, X.; Liu, C.; and Song, D. 2016 · 2016
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Deepfool: A Simple and Accurate Method to Fool Deep Neural Networks
Moosavi-Dezfooli, S.-M.; Fawzi, A.; and Frossard, P. 2016 · 2016
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Brown, T.; Mane, D.; Roy, A.; Abadi, M.; and Gilmer, J. 2017 · 2017
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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Carlini, N.; and Wagner, D. 2017 · 2017
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Detecting Adversarial Example Attacks to Deep Neural Networks
Carrara, F.; Falchi, F.; Caldelli, R.; Amato, G.; Fumarola, R.; and Becarelli, R. 2017 · 2017
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Detecting Adversarial Samples from Artifacts
Feinman, R.; Curtin, R. R.; Shintre, S.; and Gardner, A. B. 2017 · 2017
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On the (Statistical) Detection of Adversarial Examples
Grosse, K.; Manoharan, P.; Papernot, N.; Backes, M.; and McDaniel, P. 2017 · 2017
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Adversarial Examples in the Physical World
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2017 · 2017
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Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Liao, F.; Liang, M.; Dong, Y.; Pang, T.; Hu, X.; and Zhu, J. 2018 · 2018
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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Ma, X.; Li, B.; Wang, Y.; Erfani, S. M.; Wijewickrema, S.; Schoenebeck, G.; Song, D.; Houle, M. E.; and Bailey, J. 2018 · 2018
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Classification Uncertainty of Deep Neural Networks based on Gradient Information
Oberdiek, P.; Rottmann, M.; and Gottschalk, H. 2018 · 2018
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Adversarially Robust Generalization Requires More Data
Schmidt, L.; Santurkar, S.; Tsipras, D.; Talwar, K.; and Madry, A. 2018 · 2018
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On the Suitability of Lp-Norms for Creating and Preventing Adversarial Examples
Sharif, M.; Bauer, L.; and Reiter, M. K. 2018 · 2018
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Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics
Li, X.; and Li, F. 2017 · 2017
Cited alongside, same era.
On Detecting Adversarial Perturbations
Metzen, J. H.; Genewein, T.; Fischer, V.; and Bischoff, B. 2017 · 2017
Cited alongside, same era.
Analysis of Universal Adversarial Perturbations
Moosavi-Dezfooli, S.-M.; Fawzi, A.; Fawzi, O.; Frossard, P.; and Soatto, S. 2017 · 2017
Cited alongside, same era.
Learning the Distribution Preserving Semantic Subspace for Clustering
Tian, J.; Zhang, T.; Qin, A.; Shang, Z.; and Tang, Y. Y. 2017 · 2017
Cited alongside, same era.
The Space of Transferable Adversarial Examples
Tramèr, F.; Papernot, N.; Goodfellow, I.; Boneh, D.; and McDaniel, P. 2017 · 2017
Cited alongside, same era.
Defense Against Universal Adversarial Perturbations
Akhtar, N.; Liu, J.; and Mian, A. 2018 · 2018
Cited alongside, same era.
Decision based Adversarial Attacks: Reliable Attacks against Black-Box Machine Learning Models
Brendel, W.; Rauber, J.; and Bethge, M. 2018 · 2018
Cited alongside, same era.
Smith, L.; and Gal, Y. 2018 · 2018
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Pixeldefend: Leveraging Generative Models to Understand and Defend Against Adversarial Examples
Song, Y.; Kim, T.; Nowozin, S.; Ermon, S.; and Kushman, N. 2018 · 2018
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The Space of Transferable Adversarial Examples
Tramèr, F.; Papernot, N.; Goodfellow, I.; Boneh, D.; and McDaniel, P. 2018 · 2018
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Xu, W.; Evans, D.; and Qi, Y. 2018 · 2018
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Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
Aigrain, J.; and Detyniecki, M. 2019 · 2019
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Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Crecchi, F.; Bacciu, D.; and Biggio, B. 2019 · 2019
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Intrinsic Dimension Estimation for Locally Undersampled Data
Erba, V.; Gherardi, M.; and Rotondo, P. 2019 · 2019
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Pywavelets: A Python Package for Wavelet Analysis
Lee, G.; Gommers, R.; Waselewski, F.; Wohlfahrt, K.; and O’Leary, A. 2019 · 2019
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Detection Based Defense against Adversarial Examples from the Steganalysis Point of View
Liu, J.; Zhang, W.; Zhang, Y.; Hou, D.; Liu, Y.; Zha, H.; and Yu, N. 2019 · 2019
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DLA: Dense-Layer-Analysis for Adversarial Example Detection
Sperl, P.; Kao, C.-Y.; Chen, P.; and Böttinger, K. 2019 · 2019
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Robustness May Be at Odds with Accuracy
Tsipras, D.; Santurkar, S.; Engstrom, L.; Turner, A.; and Madry, A. 2019 · 2019
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Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples
Sun, G.; Su, Y.; Qin, C.; Xu, W.; Lu, X.; and Ceglowski, A. 2020 · 2020
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