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Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines.
Binary codes capable of correcting deletions, insertions, and reversals
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Direct loss minimization for structured prediction
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Exploring the space of adversarial images
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Deep speech 2: End-to-end speech recognition in english and mandarin
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D. Amodei, R. Anubhai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, J. Chen, M. Chrzanowski, A. Coates, G. Diamos, et al · 2015
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Analysis of classifiers’ robustness to adversarial perturbations
A. Fawzi, O. Fawzi, and P. Frossard · 2015
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
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Librispeech: an asr corpus based on public domain audio books
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur · 2015
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A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 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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Multi-scale aggregation by dilated convolutions
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Parseval networks: Improving robustness to adversarial examples
M. Cisse, P. Bojanowski, E. Grave, Y. Dauphin, and N. Usunier · 2017
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Adversarial examples for semantic segmentation and object detection
C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. L. Yuille · 2017
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