Fetching the paper…
Reading the bibliography…
While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input.
Semantic object classes in video: A high-definition ground truth database
G. J. Brostow, J. Fauqueur, and R. Cipolla · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Earlier work this paper cites.
Semantic image segmentation via deep parsing network
Z. Liu, X. Li, P. Luo, C. C. Loy, and X. Tang · 2015
Earlier work this paper cites.
Fully Convolutional Networks for Semantic Segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Earlier work this paper cites.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
A. Simonyan, Karen amd Zisserman · 2015
Earlier work this paper cites.
Conditional random fields as recurrent neuronal networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. S. Torr · 2015
Earlier work this paper cites.
Towards Evaluating the Robustness of Neural Networks
N. Carlini and D. Wagner · 2016
Cited alongside, same era.
Attention to scale: Scale-aware semantic image segmentation
L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Cited alongside, same era.
Robustness of classifiers: from adversarial to random noise
A. Fawzi, S.-M. Moosavi-Dezfooli, and P. Frossard · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
Later among the works it cites.
Improving the Robustness of Deep Neural Networks via Stability Training
S. Zheng, Y. Song, T. Leung, and I. Goodfellow · 2016
Later among the works it cites.
Adversarial Deep Structural Networks for Mammographic Mass Segmentation
W. Zhu, X. Xiang, T. D. Tran, and X. Xie · 2016
Later among the works it cites.
Detecting Adversarial Samples from Artifacts
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner · 2017
Closest in time.
Adversarial Examples for Semantic Image Segmentation
V. Fischer, M. C. Kumar, J. H. Metzen, and T. Brox · 2017
Closest in time.
Adversarial Machine Learning at Scale
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
DeepFool: A simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2016
Cited alongside, same era.
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.
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
Z. Wu, C. Shen, and A. v. d. Hengel · 2016
Cited alongside, same era.
A. Kurakin, I. Goodfellow, and S. Bengio · 2017
Closest in time.
On Detecting Adversarial Perturbations
J. H. Metzen, T. Genewein, V. Fischer, and B. Bischoff · 2017
Closest in time.
Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
Closest in time.
Adversarial Examples for Semantic Segmentation and Object Detection
C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. Yuille · 2017
Closest in time.
Pyramid Scene Parsing Network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Closest in time.