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We present a method to create universal, robust, targeted adversarial image patches in the real world.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 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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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 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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The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, 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
Cited alongside, same era.
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
Cited alongside, same era.
Synthesizing robust adversarial examples
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok · 2017
Cited alongside, same era.
Robust physical-world attacks on deep learning models
I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li, A. Prakash, A. Rahmati, and D. Song · 2017
Cited alongside, same era.
One pixel attack for fooling deep neural networks
S. K. Jiawei Su, Danilo Vasconcellos Vargas · 2017
Towards deep learning models resistant to adversarial examples
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Breaking the Madry Defense model with L1-based adversarial examples
Y. Sharma and P.-Y. Chen · 2017
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Ensemble adversarial training: Attacks and defenses
F. Tramér, A. Kurakin, N. Papernot, D. Boneh, and P. McDaniel · 2017
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Adversarial spheres
Anonymous · 2018
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Thermometer encoding: One hot way to resist adversarial examples
Anonymous · 2018
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Cited alongside, same era.