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Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability.
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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.
Analysis of classifiers’ robustness to adversarial perturbations
A. Fawzi, O. Fawzi, and P. Frossard · 2015
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Learning with a strong adversary
R. Huang, B. Xu, D. Schuurmans, and C. Szepesvári · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Cited alongside, same era.
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. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Robustness of classifiers: from adversarial to random noise
A. Fawzi, S. Moosavi-Dezfooli, and P. Frossard · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
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Fine-grained recognition in the noisy wild: Sensitivity analysis of convolutional neural networks approaches
E. Rodner, M. Simon, R. Fisher, and J. Denzler · 2016
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Adversarial diversity and hard positive generation
A. Rozsa, E. M. Rudd, and T. E. Boult · 2016
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Adversarial manipulation of deep representations
S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet · 2016
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Measuring neural net robustness with constraints
O. Bastani, Y. Ioannou, L. Lampropoulos, D. Vytiniotis, A. Nori, and A. Criminisi · 2016
Cited alongside, same era.
Exploring the space of adversarial images
P. Tabacof and E. Valle · 2016
Closest in time.