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Recent research has revealed that the output of Deep Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input vector.
Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation
N. Hansen and A. Ostermeier · 1996
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Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
R. Storn and K. Price · 1997
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Self-adaptive differential evolution algorithm for numerical optimization
A.K. Qin and P.N. Suganthan · 2005
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Can machine learning be secure?
M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar · 2006
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Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems
J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer · 2006
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The CMA evolution strategy: a comparing review
N. Hansen · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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The security of machine learning
M. Barreno, B. Nelson, A. D. Joseph, and J. Tygar · 2010
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Differential evolution: A survey of the state-of-the-art
S. Das and P. N. Suganthan · 2011
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A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms
P. Civicioglu and E. Besdok · 2013
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M. Lin, Q. Chen, and S. Yan · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Intriguing properties of neural networks
S. Christian, Z. Wojciech, S. Ilya, b. Joan, E. Dumitru, G. Ian, F. Rob · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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General subpopulation framework and taming the conflict inside populations
D. V. Vargas, J. Murata, H. Takano, and A. C. B. Delbem · 2015
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Understanding intra-class knowledge inside cnn
D. Wei, B. Zhou, A. Torrabla, and W. Freeman · 2015
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Understanding neural networks through deep visualization
J. Yosinski, J. Clune, T. Fuchs, and H. Lipson · 2015
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Classification regions of deep neural networks
A. Fawzi, S.-M. Moosavi-Dezfooli, P. Frossard, and S. Soatto · 2017
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Universal adversarial perturbations
S. M. Moosavi Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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Simple black-box adversarial attacks on deep neural networks
N. Narodytska and S. Kasiviswanathan · 2017
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Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
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Spectrum-diverse neuroevolution with unified neural models
D. V. Vargas and J. Murata · 2017
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R. Huang, B. Xu, D. Schuurmans, C. Szepesvári · 2015
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Novelty-organizing team of classifiers in noisy and dynamic environments
D.V. Vargas, H. Takano and J. Murata · 2015
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
S. M. Moosavi Dezfooli, F. Alhussein and F. Pascal · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
Cited alongside, same era.
Adversarial diversity and hard positive generation
A. Rozsa, E. M. Rudd, and T. E. Boult · 2016
Cited alongside, same era.
Crafting Adversarial Input Sequences for Recurrent Neural Networks
P. Nicolas, M. Patrick, S. Ananthram, H. Richard · 2016
Cited alongside, same era.
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
G. Kathrin, P. Nicolas, M. Praveen, B. Michael, M. Patrick · 2016
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J. Su, D. Vargas, and K. Sakurai · 2017
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A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
E. Logan, T. Brandon, T. Dimitris, S. Ludwig, M. Aleksander · 2017
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Adversarial Examples: Attacks and Defenses for Deep Learning
X. Yuan, P. He, Q. Zhu, R. R. Bhat · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
W. Xu, D. Evans, Y. Qi · 2017
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Detecting Adversarial Examples in Deep Networks with Adaptive Noise Reduction
B. Liang et al · 2017
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Towards evaluating the robustness of neural networks
N. Carlini, D. Wagner · 2017
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Magnet and efficient defenses against adversarial attacks are not robust to adversarial examples
N. Carlini and D. Wagner · 2017
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Did you hear that? Adversarial Examples Against Automatic Speech Recognition
A. Moustafa, B. Bharathan, S. Mani · 2018
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On the Suitability of Lp-norms for Creating and Preventing Adversarial Examples
M. Sharif, L. Bauer, MK. Reiter · 2018
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CIFAR-10 - Object Recognition in Images@Kaggle
2018
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One Pixel Attack for Fooling Deep Neural Networks
Jiawei Su, Danilo Vasconcellos Vargas, Kouichi Sakurai · 2019
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