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We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
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Exposing photo manipulation with inconsistent shadows
E. Kee, J. F. O’Brien, and H. Farid · 2013
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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High-resolution stereo datasets with subpixel-accurate ground truth
D. Scharstein, H. Hirschmüller, Y. Kitajima, G. Krathwohl, N. Nešić, X. Wang, and P. Westling · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Distributional smoothing with virtual adversarial training
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N. Papernot, P. McDaniel, and I. Goodfellow · 2016
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Practical black-box attacks against deep learning systems using adversarial examples
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
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Defensive distillation is not robust to adversarial examples
N. Carlini and D. Wagner
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N. Papernot, P. D. McDaniel, and I. J. Goodfellow · 2016
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Understanding deep learning requires rethinking generalization
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How to detect faked photos
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No need to worry about adversarial examples in object detection in autonomous vehicles
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J. H. Metzen, T. Genewein, V. Fischer, and B. Bischoff · 2017
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