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Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input.
Controller design for quadrotor uavs using reinforcement learning
H. Bou-Ammar, H. Voos, and W. Ertel · 2010
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Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng · 2011
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Traffic sign recognition with multi-scale convolutional networks
P. Sermanet and Y. LeCun · 2011
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Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey
A. Mogelmose, M. M. Trivedi, and T. B. Moeslund · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2012
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Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Feature cross-substitution in adversarial classification
B. Li and Y. Vorobeychik · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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Scalable optimization of randomized operational decisions in adversarial classification settings
B. Li and Y. Vorobeychik · 2015
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Cited alongside, same era.
Adversarial manipulation of deep representations
S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet · 2015
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
Later among the works it cites.
Stealing machine learning models via prediction apis
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
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p2-trafficsigns
V. Yadav · 2016
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Robust adversarial examples
A. Athalye · 2017
Closest in time.
Synthesizing robust adversarial examples
A. Athalye and I. Sutskever · 2017
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Cited alongside, same era.
Towards vision-based deep reinforcement learning for robotic motion control
F. Zhang, J. Leitner, M. Milford, B. Upcroft, and P. Corke · 2015
Cited alongside, same era.
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2016
Cited alongside, same era.
Universal adversarial perturbations
S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
Uav-based autonomous image acquisition with multi-view stereo quality assurance by confidence prediction
C. Mostegel, M. Rumpler, F. Fraundorfer, and H. Bischof · 2016
Cited alongside, same era.
cleverhans v1.0.0: an adversarial machine learning library
N. Papernot, I. Goodfellow, R. Sheatsley, R. Feinman, and P. McDaniel · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N. Papernot, P. McDaniel, and I. Goodfellow · 2016
Cited alongside, same era.
M. Cisse, Y. Adi, N. Neverova, and J. Keshet · 2017
Closest in time.
Note on Attacking Object Detectors with Adversarial Stickers
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, D. Song, T. Kohno, A. Rahmati, A. Prakash, and F. Tramer · 2017
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Adversarial examples for generative models
J. Kos, I. Fischer, and D. Song · 2017
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No need to worry about adversarial examples in object detection in autonomous vehicles
J. Lu, H. Sibai, E. Fabry, and D. Forsyth · 2017
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Universal adversarial perturbations against semantic image segmentation
J. H. Metzen, M. C. Kumar, T. Brox, and V. Fischer · 2017
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Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 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. Yuille · 2017
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