Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems

J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,” Neural networks , vol. 32, pp. 323–332, 2012

2012

Earlier work this paper cites.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013

Original

2013

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014

Original

2014

Earlier work this paper cites.

G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531 , 2015

Original

2015

Earlier work this paper cites.

S. Zheng, Y. Song, T. Leung, and I. Goodfellow, “Improving the robustness of deep neural networks via stability training,” in Proceedings of the ieee conference on computer vision and pattern recognition , 2016, pp. 4480–4488

2016

Earlier work this paper cites.

S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2574–2582

2016

Earlier work this paper cites.

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” arXiv preprint arXiv:1611.01236 , 2016

Original

2016

Earlier work this paper cites.

N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in 2016 IEEE Symposium on Security and Privacy (SP) . IEEE, 2016, pp. 582–597

2016

Earlier work this paper cites.

D. Warde-Farley and I. Goodfellow, “11 adversarial perturbations of deep neural networks,” Perturbations, Optimization, and Statistics , p. 311, 2016

2016

Earlier work this paper cites.

N. Papernot, P. McDaniel, and I. Goodfellow, “Transferability in machine learning: from phenomena to black-box attacks using adversarial samples,” arXiv preprint arXiv:1605.07277 , 2016

Original

2016

Earlier work this paper cites.

N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 2016, pp. 372–387

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Earlier work this paper cites.

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” arXiv preprint arXiv:1607.02533 , 2016

Original

2016

Earlier work this paper cites.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788

2016

Earlier work this paper cites.

V. Zantedeschi, M.-I. Nicolae, and A. Rawat, “Efficient defenses against adversarial attacks,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security . ACM, 2017, pp. 39–49

2017

Cited alongside, same era.

C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 1321–1330

2017

Cited alongside, same era.

N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 39–57

2017

Cited alongside, same era.

C. Guo, M. Rana, M. Cisse, and L. van der Maaten, “Countering adversarial images using input transformations,” arXiv preprint arXiv:1711.00117 , 2017

Original

2017

Cited alongside, same era.

S. Tian, G. Yang, and Y. Cai, “Detecting adversarial examples through image transformation,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018

2018

Later among the works it cites.

S. Jha, U. Jang, S. Jha, and B. Jalaian, “Detecting adversarial examples using data manifolds,” in IEEE Military Communications Conference (MILCOM) , Norfolk, VA, 2018, pp. 547–552

2018

Later among the works it cites.

T. Pang, C. Du, Y. Dong, and J. Zhu, “Towards robust detection of adversarial examples,” in Advances in Neural Information Processing Systems , 2018, pp. 4584–4594

2018

Later among the works it cites.

J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, “Adversarial spheres,” arXiv preprint arXiv:1801.02774 , 2018

Original

2018

Later among the works it cites.

S. Chen, X. Huang, Z. He, and C. Sun, “Damagenet: A universal adversarial dataset,” arXiv preprint arXiv:1912.07160 , 2019