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Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images.
Computing the maximum overlap of two convex polygons under translations
M. De Berg, O. Cheong, O. Devillers, M. Van Kreveld, and M. Teillaud · 1998
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Biometric person recognition: Face, speech and fusion
C. Sanderson · 2008
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Distance-based image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Foveation-based mechanisms alleviate adversarial examples
Y. Luo, X. Boix, G. Roig, T. Poggio, and Q. Zhao · 2015
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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 learning applications and challenges in big data analytics
M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 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
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A discriminative feature learning approach for deep face recognition
Y. Wen, K. Zhang, Z. Li, and Y. Qiao · 2016
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How drive. ai is mastering autonomous driving with deep learning
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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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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Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, L. Chen, M. E. Kounavis, and D. H. Chau · 2017
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Countering adversarial images using input transformations
C. Guo, M. Rana, M. Cisse, and L. van der Maaten · 2017
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Boosting adversarial attacks with momentum
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li · 2018
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Motivating the rules of the game for adversarial example research
J. Gilmer, R. P. Adams, I. Goodfellow, D. Andersen, and G. E. Dahl · 2018
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Improving dnn robustness to adversarial attacks using jacobian regularization
D. Jakubovitz and R. Giryes · 2018
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H. Kannan, A. Kurakin, and I. Goodfellow · 2018
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Adversarial attacks and defences competition
A. Kurakin, I. Goodfellow, S. Bengio, Y. Dong, F. Liao, M. Liang, T. Pang, J. Zhu, X. Hu, C. Xie, et al · 2018
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J. Z. Kolter and E. Wong · 2017
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Defense against adversarial attacks using high-level representation guided denoiser
F. Liao, M. Liang, Y. Dong, T. Pang, J. Zhu, and X. Hu · 2017
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Cascade adversarial machine learning regularized with a unified embedding
T. Na, J. H. Ko, and S. Mukhopadhyay · 2017
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Certifiable distributional robustness with principled adversarial training
A. Sinha, H. Namkoong, and J. Duchi · 2017
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Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel · 2017
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The space of transferable adversarial examples
F. Tramèr, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel · 2017
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Defensive quantization: When efficiency meets robustness
J. Lin, C. Gan, and S. Han · 2018
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Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
A. S. Ross and F. Doshi-Velez · 2018
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Improving the generalization of adversarial training with domain adaptation
C. Song, K. He, L. Wang, and J. E. Hopcroft · 2018
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Mitigating adversarial effects through randomization
C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille · 2018
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Improving transferability of adversarial examples with input diversity
C. Xie, Z. Zhang, J. Wang, Y. Zhou, Z. Ren, and A. Yuille · 2018
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Interpreting adversarial robustness: A view from decision surface in input space
F. Yu, C. Liu, Y. Wang, and X. Chen · 2018
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Image super-resolution as a defense against adversarial attacks
A. Mustafa, S. H. Khan, M. Hayat, J. Shen, and L. Shao · 2019
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Improving adversarial robustness via promoting ensemble diversity
T. Pang, K. Xu, C. Du, N. Chen, and J. Zhu · 2019
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