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Despite the rapid development of adversarial machine learning, most adversarial attack and defense researches mainly focus on the perturbation-based adversarial examples, which is constrained by the input images.
Backpropagation applied to handwritten zip code recognition
LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L · 1989
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Visualizing Data using t-SNE
Maaten, L. and Hinton, G · 2008
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Explaining and Harnessing Adversarial Examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2015
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Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford, A., Metz, L., and Chintala, S · 2016
Earlier work this paper cites.
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Exploring the Space of Black-box Attacks on Deep Neural Networks
Bhagoji, A., He, W., Li, B., and Song, D · 2017
Cited alongside, same era.
Towards Evaluating the Robustness of Neural Networks
Carlini, N. and Wagner, D · 2017
Cited alongside, same era.
Improved Training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
Cited alongside, same era.
Adversarial Machine Learning at Scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2017
Cited alongside, same era.
Delving into Transferable Adversarial Examples and Black-box Attacks
Robust Physical-World Attacks on Deep Learning Models
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2018
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Countering Adversarial Images using Input Transformations
Guo, C., Rana, M., Cisse, M., and Maaten, L · 2018
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Decision Boundary Analysis of Adversarial Examples
He, W., Li, B., and Song, D · 2018
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Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., and Zhu, 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
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Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
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Liu, Y., Chen, X., Liu, C., and Song, D · 2017
Cited alongside, same era.
On Detecting Adversarial Perturbations
Metzen, Hendrik, J., Genewein, T., Fischer, V., and Bischoff, B · 2017
Cited alongside, same era.
Conditional Image Synthesis With Auxiliary Classifier GANs
Odena, A., Olah, C., and Shlens, J · 2017
Cited alongside, same era.
APE-GAN: Adversarial Perturbation Elimination with GAN
Shen, S., Jin, G., Gao, K., and Zhang, Y · 2017
Cited alongside, same era.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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
Cited alongside, same era.
Thermometer Encoding: One Hot Way To Resist Adversarial Examples
Buckman, J., Roy, A., Raffel, C., and Goodfellow, I · 2018
Cited alongside, same era.
Samangouei, P., Kabkab, M., and Chellappa, R · 2018
Later among the works it cites.
Constructing unrestricted adversarial examples with generative models
Song, Y., Shu, R., Kushman, N., and Ermon, S · 2018
Later among the works it cites.
Ensemble Adversarial Training: Attacks and Defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2018
Later among the works it cites.
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Cao, Y., Xiao, C., Yang, D., Fang, J., Yang, R., Liu, M., and Li, B · 2019
Closest in time.
A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance
Shamir, A., Safran, I., Ronen, E., and Dunkelman, O · 2019
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Improving the Generalization of Adversarial Training with Domain Adaptation
Song, C., He, K., Wang, L., and Hopcroft, J · 2019
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