Fetching the paper…
Reading the bibliography…
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques.
Interpreting adversarial examples by activation promotion and suppression
Xu, K., Liu, S., Zhang, G., Sun, M., Zhao, P., Fan, Q., Gan, C., and Lin, X · 1904
Earlier work this paper cites.
Natural evolution strategies
Wierstra, D., Schaul, T., Peters, J., and Schmidhuber, J · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2010
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., and Kingsbury, B · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A · 2015
Earlier work this paper cites.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Liu, Y., Chen, X., Liu, C., and Song, D · 2016
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A · 2016
Earlier work this paper cites.
Towards robust deep neural networks with bang
Rozsa, A., Gunther, M., and Boult, T. E · 2016
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., and Darrell, T · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Earlier work this paper cites.
Zagoruyko, S. and Komodakis, N · 2016
Earlier work this paper cites.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Maximum resilience of artificial neural networks
Cheng, C.-H., Nührenberg, G., and Ruess, H · 2017
Cited alongside, same era.
Deep neural networks as 0-1 mixed integer linear programs: A feasibility study
Fischetti, M. and Jo, J · 2017
Cited alongside, same era.
Countering adversarial images using input transformations
Guo, C., Rana, M., Cisse, M., and van der Maaten, L · 2018
Later among the works it cites.
Black-box adversarial attacks with limited queries and information
Ilyas, A., Engstrom, L., Athalye, A., and Lin, J · 2018
Later among the works it cites.
How local is the local diversity? reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization
Li, Y., Wang, L., Yang, T., and Gong, B · 2018
Later among the works it cites.
Defense against adversarial attacks using high-level representation guided denoiser
Liao, F., Liang, M., Dong, Y., Pang, T., Zhu, J., and Hu, X · 2018
Later among the works it cites.
Towards robust neural networks via random self-ensemble
Liu, X., Cheng, M., Zhang, H., and Hsieh, C.-J · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Vqs: Linking segmentations to questions and answers for supervised attention in vqa and question-focused semantic segmentation
Gan, C., Li, Y., Li, H., Sun, C., and Gong, B · 2017
Cited alongside, same era.
Adversarial example defenses: Ensembles of weak defenses are not strong
He, W., Wei, J., Chen, X., Carlini, N., and Song, D · 2017
Cited alongside, same era.
An approach to reachability analysis for feed-forward relu neural networks
Lomuscio, A. and Maganti, L · 2017
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A · 2017
Cited alongside, same era.
Evolution strategies as a scalable alternative to reinforcement learning
Salimans, T., Ho, J., Chen, X., and Sutskever, I · 2017
Cited alongside, same era.
Breaking the madry defense model with l _ 1 l\_1 -based adversarial examples
Sharma, Y. and Chen, P.-Y · 2017
Cited alongside, same era.
Ma, X., Li, B., Wang, Y., Erfani, S. M., Wijewickrema, S., Houle, M. E., Schoenebeck, G., Song, D., and Bailey, J · 2018
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Later among the works it cites.
Cascade adversarial machine learning regularized with a unified embedding
Na, T., Ko, J. H., and Mukhopadhyay, S · 2018
Later among the works it cites.
Deflecting adversarial attacks with pixel deflection
Prakash, A., Moran, N., Garber, S., DiLillo, A., and Storer, J · 2018
Later among the works it cites.
Tsuzuku, Y., Sato, I., and Sugiyama, M · 2018
Later among the works it cites.
Towards fast computation of certified robustness for relu networks
Weng, T.-W., Zhang, H., Chen, H., Song, Z., Hsieh, C.-J., Boning, D., Dhillon, I. S., and Daniel, L · 2018
Later among the works it cites.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, Z · 2018
Later among the works it cites.
Mitigating adversarial effects through randomization
Xie, C., Wang, J., Zhang, Z., Ren, Z., and Yuille, A · 2018
Later among the works it cites.
Efficient neural network robustness certification with general activation functions
Zhang, H., Weng, T.-W., Chen, P.-Y., Hsieh, C.-J., and Daniel, L · 2018
Later among the works it cites.
Query-efficient hard-label black-box attack: An optimization-based approach
Cheng, M., Le, T., Chen, P.-Y., Zhang, H., Yi, J., and Hsieh, C.-J · 2019
Closest in time.
Adv-BNN: Improved adversarial defense through robust bayesian neural network
Liu, X., Li, Y., Wu, C., and Hsieh, C.-J · 2019
Closest in time.
A direct approach to robust deep learning using adversarial networks
Wang, H. and Yu, C.-N · 2019
Closest in time.