Fetching the paper…
Reading the bibliography…
Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results.
1904
Earlier work this paper cites.
A. Sung, “Ranking importance of input parameters of neural networks,” Expert systems with Applications , vol. 15, no. 3-4, pp. 405–411, 1998
1998
Earlier work this paper cites.
J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson et al. , “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nature medicine , vol. 7, no. 6, pp. 673–679, 2001
2001
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
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 , 2012
2012
Earlier work this paper cites.
A. Mohamed, G. E. Dahl, and G. E. Hinton, “Acoustic modeling using deep belief networks,” IEEE Trans. Audio, Speech & Language Processing , 2012
2012
Earlier work this paper cites.
H. Xu and S. Mannor, “Robustness and generalization,” Machine learning , 2012
2012
Earlier work this paper cites.
D. Chen and C. Manning, “A fast and accurate dependency parser using neural networks,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in Neural Information Processing Systems , 2014, pp. 3104–3112
2014
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in ECCV , 2014
2014
Earlier work this paper cites.
B. Zhou, À. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, “Learning deep features for scene recognition using places database,” in Advances in Neural Information Processing Systems , 2014
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
B. Zhou, A. Khosla, À. Lapedriza, A. Oliva, and A. Torralba, “Object detectors emerge in deep scene cnns,” in International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li, “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision , 2015
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
N. Papernot, P. D. 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 (S&P) , 2016
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2016
2016
Earlier work this paper cites.
Z. Chi, H. Li, H. Lu, and M.-H. Yang, “Dual deep network for visual tracking,” IEEE Transactions on Image Processing , vol. 26, no. 4, pp. 2005–2015, 2017
2017
Earlier work this paper cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in IEEE Symposium on Security and Privacy (S&P) , 2017
2017
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” in International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” in International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3142–3155, July 2017
2017
Cited alongside, same era.
J. Lu, T. Issaranon, and D. A. Forsyth, “Safetynet: Detecting and rejecting adversarial examples robustly,” in Proceedings of the IEEE International Conference on Computer Vision , 2017
2017
Cited alongside, same era.
2018
Later among the works it cites.
A. Athalye, N. Carlini, and D. Wagner, “Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,” in Proceedings of the 35th International Conference on Machine Learning , 2018
2018
Later among the works it cites.
U. Shaham, Y. Yamada, and S. Negahban, “Understanding adversarial training: Increasing local stability of supervised models through robust optimization,” Neurocomputing , 2018
2018
Later among the works it cites.
J. Fu, J. Liu, Y. Wang, J. Zhou, C. Wang, and H. Lu, “Stacked deconvolutional network for semantic segmentation,” IEEE Transactions on Image Processing , 2019
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba, “Network dissection: Quantifying interpretability of deep visual representations,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017
2017
Cited alongside, same era.
J. Cai, S. Gu, and L. Zhang, “Learning a deep single image contrast enhancer from multi-exposure images,” IEEE Transactions on Image Processing , vol. 27, no. 4, pp. 2049–2062, 2018
2018
Cited alongside, same era.
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018
2018
Cited alongside, same era.
D. Song, K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, F. Tramèr, A. Prakash, and T. Kohno, “Physical adversarial examples for object detectors,” in WOOT , 2018
2018
Cited alongside, same era.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
C. Guo, J. Gardner, Y. You, A. G. Wilson, and K. Weinberger, “Simple black-box adversarial attacks,” in Proceedings of the 34th International Conference on Machine Learning , 2019
2019
Closest in time.
A. Liu, X. Liu, J. Fan, Y. Ma, A. Zhang, H. Xie, and D. Tao, “Perceptual-sensitive gan for generating adversarial patches,” in AAAI , 2019
2019
Closest in time.
2019
Closest in time.
K. Roth, Y. Kilcher, and T. Hofmann, “The odds are odd: A statistical test for detecting adversarial examples,” in Proceedings of the 36th International Conference on Machine Learning , 2019
2019
Closest in time.
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry, “Robustness may be at odds with accuracy,” in International Conference on Learning Representations , 2019
2019
Closest in time.
2019
Closest in time.
——, “Learning reliable visual saliency for model explanations,” IEEE Transactions on Multimedia , 2019
2019
Closest in time.
Y. Li, L. Li, L. Wang, T. Zhang, and B. Gong, “NATTACK: learning the distributions of adversarial examples for an improved black-box attack on deep neural networks,” in International Conference on Machine Learning , 2019
2019
Closest in time.
D. Hendrycks and T. G. Dietterich, “Benchmarking neural network robustness to common corruptions and perturbations,” in 7th ICLR, International Conference on Learning Representations 2019, New Orleans, LA, USA, May 6-9, 2019 , 2019
2019
Closest in time.
D. Yin, R. G. Lopes, J. Shlens, E. D. Cubuk, and J. Gilmer, “A fourier perspective on model robustness in computer vision,” in Advances in Neural Information Processing Systems , 2019, pp. 13 255–13 265
2019
Closest in time.
C. Zhang, S. Bengio, and Y. Singer, “Are all layers created equal?” CoRR , vol. abs/1902.01996, 2019
2019
Closest in time.
L. Li, Y. Dong, W. Ren, J. Pan, C. Gao, N. Sang, and M. Yang, “Semi-supervised image dehazing,” IEEE Transactions on Image Processing , 2020
2020
Closest in time.
Y. Duan, J. Lu, W. Zheng, and J. Zhou, “Deep adversarial metric learning,” IEEE Transactions on Image Processing , vol. 29, pp. 2037–2051, 2020
2020
Closest in time.
W. Chen, Z. Zhang, X. Hu, and B. Wu, “Boosting decision-based black-box adversarial attacks with random sign flip,” in European Conference on Computer Vision , 2020
2020
Closest in time.
A. Liu, T. Huang, X. Liu, Y. Xu, Y. Ma, X. Chen, S. Maybank, and D. Tao, “Spatiotemporal attacks for embodied agents,” in European Conference on Computer Vision , 2020
2020
Closest in time.
A. Liu, J. Wang, X. Liu, B. Cao, C. Zhang, and H. Yu, “Bias-based universal adversarial patch attack for automatic check-out,” in European Conference on Computer Vision , 2020
2020
Closest in time.
——, “Interpret neural networks by extracting critical subnetworks,” IEEE Transactions on Image Processing , 2020
2020
Closest in time.