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Despite being popularly used in many applications, neural network models have been found to be vulnerable to adversarial examples, i.e., carefully crafted examples aiming to mislead machine learning models.
The MNIST database of handwritten digits
Yann LeCun, Corinna Cortes, and Christopher JC Burges. 1998 · 1998
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The CIFAR-10 dataset
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2013a · 2013
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
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Per Larsen, Andrei Homescu, Stefan Brunthaler, and Michael Franz. 2014 · 2014
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Defensive Distillation is Not Robust to Adversarial Examples
Nicholas Carlini and David A. Wagner. 2016 · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016 · 2016
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Conditional Image Generation with PixelCNN Decoders. In Proc. NIPS . 4797–4805
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. 2016 · 2016
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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick D. McDaniel, and Ian J. Goodfellow. 2016a · 2016
Cited alongside, same era.
Dense Associative Memory is Robust to Adversarial Inputs
Dmitry Krotov and John J. Hopfield. 2017 · 2017
Cited alongside, same era.
Feature-guided black-box safety testing of deep neural networks. In Proc. ICCV . 446–454
Jiajun Lu, Theerasit Issaranon, and David Alexander Forsyth. 2017 · 2017
Cited alongside, same era.
MagNet: A Two-Pronged Defense Against Adversarial Examples. In Proc. CCS . 135–147
Dongyu Meng and Hao Chen. 2017 · 2017
Cited alongside, same era.
Simple Black-Box Adversarial Perturbations for Deep Networks. In Proc. CVPRW . 1310–1318
Nina Narodytska and Shiva Prasad Kasiviswanathan. 2017 · 2017
Cited alongside, same era.
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning. In Proc. ICCAD
Rouhani Bita Darvish, Samragh Mohammad, Javaheripi Mojan, Javidi Tara, and Koushanfar Farinaz. 2018 · 2018
Closest in time.
TensorFlow
Google. 2018 · 2018
Closest in time.
Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Closest in time.
Defense-GAN: Protecting classifiers against adversarial attacks using generative models. In Proc. ICLR
Pouya Samangouei, Maya Kabkab, and Rama Chellappa. 2018 · 2018
Closest in time.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples. In Proc. ICLR
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman. 2018 · 2018
Closest in time.
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Towards evaluating the robustness of neural networks. In Proc. Security and Privacy (SP) . 39–57
David Wagner Nicholas Carlini. 2017 · 2017
Cited alongside, same era.
cleverhans v2.0.0: an adversarial machine learning library
Nicolas Papernot, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Fartash Faghri, Alexander Matyasko, Karen Hambardzumyan, Yi-Lin Juang, Alexey Kurakin, Ryan Sheatsley, Abhibhav Garg, and Yen-Chen Lin. 2017a · 2017
Cited alongside, same era.
Efficient Defenses Against Adversarial Attacks. In Proc. AISec
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat. 2017 · 2017
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. In Proc. ICML . 274–283
Anish Athalye, Nicholas Carlini, and David Wagner. 2018 · 2018
Cited alongside, same era.
Practical Black-Box Attacks Against Machine Learning. In Proc. ASIA CCS . 506–519
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami. 2017b
Cited in the paper.
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. In Proc. SP . 582–597
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2016c
Cited in the paper.
The Limitations of Deep Learning in Adversarial Settings. In Proc. EuroSP . 372–387
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. 2016b
Cited in the paper.
Multiple-Implementation Testing of Supervised Learning Software. In Proc. EDSMLS
Siwakorn Srisakaokul, Zhengkai Wu, Angello Astorga, Oreoluwa Alebiosu, , and Tao Xie. 2018 · 2018
Closest in time.
Cascade Adversarial Machine Learning Regularization with a Unified Embedding. In Proc. ICLR
Saibal Mukhopadhyay Taesik Na, Jong Hwan Ko. 2018 · 2018
Closest in time.
Ensemble Adversarial Training: Attacks and Defenses. In Proc. ICLR
Florian Tramer, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2018 · 2018
Closest in time.
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. In Proc. NDSS
Weilin Xu, David Evans, and Yanjun Qi. 2018 · 2018
Closest in time.