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Solving for adversarial examples with projected gradient descent has been demonstrated to be highly effective in fooling the neural network based classifiers.
Accelerated greedy algorithms for maximizing submodular set functions
Minoux, M · 1978
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An analysis of approximations for maximizing submodular set functions
Nemhauser, G. L., Wolsey, L. A., and Fisher, M. L · 1978
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A method for solving the convex programming problem with convergence rate O ( 1 / k 2 ) {O}(1/k^{2})
Nesterov, Y. E · 1983
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Theory of Linear and Integer Programming
Schrijver, A · 1986
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Statistics of natural images and models
Huang, J. and Mumford, D · 1999
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Cost-effective outbreak detection in networks
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., and Glance, N · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Maximizing non-monotone submodular functions
Feige, U., Mirrokni, V. S., and Vondrak, J · 2011
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A class of submodular functions for document summarization
Lin, H. and Bilmes, J · 2011
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Poisoning attacks against support vector machines
Biggio, B., Nelson, B., and Laskov, P · 2012
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Inferring networks of diffusion and influence
Gomez-Rodriguez, M., Leskovec, J., and Krause, A · 2012
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Learning with submodular functions: A convex optimization perspective
Bach, F. et al · 2013
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Distributed submodular maximization: Identifying representative elements in massive data
Mirzasoleiman, B., Karbasi, A., Sarkar, R., and Krause, A · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Cited alongside, same era.
Using document summarization techniques for speech data subset selection
Wei, K., Liu, Y., Kirchhoff, K., and Bilmes, J · 2013
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Cited alongside, same era.
Submodular function maximization, 2014
Krause, A. and Golovin, D · 2014
Cited alongside, same era.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P.-Y., Zhang, H., Sharma, Y., Yi, J., and Hsieh, C.-J · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A · 2017
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Lazier than lazy greedy
Mirzasoleiman, B., Badanidiyuru, A., Karbasi, A., Vondrák, J., and Krause, A · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2015
Cited alongside, same era.
Hidden voice commands
Carlini, N., Mishra, P., Vaidya, T., Zhang, Y., Sherr, M., Shields, C., Wagner, D., and Zhou, W · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Liu, Y., Chen, X., Liu, C., and Song, D · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Papernot, N., McDaniel, P., and Goodfellow, I · 2016
Cited alongside, same era.
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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Practical black-box attacks on deep neural networks using efficient query mechanisms
Bhagoji, A. N., He, W., Li, B., and Song, D · 2018
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Query-efficient hard-label black-box attack: An optimization-based approach
Cheng, M., Le, T., Chen, P.-Y., Yi, J., Zhang, H., and Hsieh, C.-J · 2018
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Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
Tu, C.-C., Ting, P., Chen, P.-Y., Liu, S., Zhang, H., Yi, J., Hsieh, C.-J., and Cheng, S.-M · 2018
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https://clarifai.com/ , 2019
Clarifai API · 2019
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https://cloud.google.com/vision/ , 2019
Google vision API · 2019
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https://www.ibm.com/watson/services/visual-recognition/ , 2019
Watson visual recognition · 2019
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