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Despite the recent advancements in deploying neural networks for image classification, it has been found that adversarial examples are able to fool these models leading them to misclassify the images.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2013
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Microsoft COCO: common objects in context
T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. M. Nguyen, J. Yosinski, and J. Clune · 2014
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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. S. Bernstein, A. C. Berg, and F. Li · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
Cited alongside, same era.
Faster R-CNN: towards real-time object detection with region proposal networks
S. Ren, K. He, R. B. Girshick, and J. Sun · 2015
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. A. Wagner · 2016
Show-and-fool: Crafting adversarial examples for neural image captioning
H. Chen, H. Zhang, P. Chen, J. Yi, and C. Hsieh · 2017
Later among the works it cites.
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick · 2017
Later among the works it cites.
Focal loss for dense object detection
T. Lin, P. Goyal, R. B. Girshick, K. He, and P. Dollár · 2017
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NO need to worry about adversarial examples in object detection in autonomous vehicles
J. Lu, H. Sibai, E. Fabry, and D. A. Forsyth · 2017
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Foolbox v0.8.0: A python toolbox to benchmark the robustness of machine learning models
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Cited alongside, same era.
cleverhans v0.1: an adversarial machine learning library
I. J. Goodfellow, N. Papernot, and P. D. McDaniel · 2016
Cited alongside, same era.
Adversarial examples in the physical world
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
J. Rauber, W. Brendel, and M. Bethge · 2017
Later among the works it cites.
Defense-GAN: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
Closest in time.
Generating adversarial examples with adversarial networks
C. Xiao, B. Li, J. Zhu, W. He, M. Liu, and D. Song · 2018
Closest in time.