Fetching the paper…
Reading the bibliography…
Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors.
Y. LeCun, C. Cortes, and C. J. Burges, “The mnist database of handwritten digits,” 1998
1998
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2009, pp. 248–255
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NIPS) , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
C. J. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in International Conference on Learning Representation (ICLR) , 2014
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 1–9
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification,” in IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 1026–1034
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representation (ICLR) , 2015
2015
Earlier work this paper cites.
S. Baluja, M. Covell, and R. Sukthankar, “The virtues of peer pressure: A simple method for discovering high-value mistakes,” in Computer Analysis of Images and Patterns (CAIP) . Springer, 2015, pp. 96–108
2015
Cited alongside, same era.
Y. Luo, X. Boix, G. Roig, T. Poggio, and Q. Zhao, “Foveation-based mechanisms alleviate adversarial examples,” 2015, under review
2015
Cited alongside, same era.
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in IEEE Symposium on Security and Privacy (SP) , 2015
2015
Cited alongside, same era.
J.-C. Chen, V. M. Patel, and R. Chellappa, “Unconstrained face verification using deep CNN features,” in IEEE Winter Conference on Applications of Computer Vision (WACV) , 2016
2016
Cited alongside, same era.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “DeepFool: a simple and accurate method to fool deep neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Closest in time.
T. Miyato, A. M. Dai, and I. Goodfellow, “Virtual adversarial training for semi-supervised text classification,” 2016, under review
2016
Closest in time.
S. Zheng, Y. Song, T. Leung, and I. Goodfellow, “Improving the robustness of deep neural networks via stability training,” 2016, under review
2016
Closest in time.
A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” Google, Tech. Rep., 2016
2016
Closest in time.
N. Papernot, P. McDaniel, and I. J. Goodfellow, “Transferability in machine learning: from phenomena to black-box attacks using adversarial samples,” 2016, under review
2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Cited alongside, same era.
A. Rozsa, E. M. Rudd, and T. E. Boult, “Adversarial diversity and hard positive generation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet, “Adversarial manipulation of deep representations,” in International Conference on Learning Representations (ICLR) , 2016
2016
Cited alongside, same era.
Closest in time.
N. Papernot, P. McDaniel, I. J. Goodfellow, S. Jha, Z. Berkay Celik, and A. Swami, “Practical black-box attacks against deep learning systems using adversarial examples,” 2016, under review
2016
Closest in time.
A. Bendale and T. Boult, “Towards open set deep networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Closest in time.