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Standard Convolutional Neural Networks (CNNs) can be easily fooled by images with small quasi-imperceptible artificial perturbations.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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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 · 2014
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Foolbox: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Cited alongside, same era.
Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
Cited alongside, same era.
Matrix capsules with em routing
Geoffrey E Hinton, Sara Sabour, and Nicholas Frosst · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Multi-level dense capsule networks
Sai Samarth R Phaye, Apoorva Sikka, Abhinav Dhall, and Deepti R Bathula · 2018
On the vulnerability of capsule networks to adversarial attacks
Felix Michels, Tobias Uelwer, Eric Upschulte, and Stefan Harmeling · 2019
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Deepcaps: Going deeper with capsule networks
Jathushan Rajasegaran, Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Suranga Seneviratne, and Ranga Rodrigo · 2019
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Boosting decision-based black-box adversarial attacks with random sign flip
Weilun Chen, Zhaoxiang Zhang, Xiaolin Hu, and Baoyuan Wu · 2020
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When nas meets robustness: In search of robust architectures against adversarial attacks
Minghao Guo, Yuzhe Yang, Rui Xu, and Ziwei Liu · 2020
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Toward adversarial robustness via semi-supervised robust training
Yiming Li, Baoyuan Wu, Yan Feng, Yanbo Fan, Yong Jiang, Zhifeng Li, and Shutao Xia · 2020
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Cited alongside, same era.
Sparse unsupervised capsules generalize better
David Rawlinson, Abdelrahman Ahmed, and Gideon Kowadlo · 2018
Cited alongside, same era.
Is robustness the cost of accuracy? - a comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
Cited alongside, same era.
Cappronet: Deep feature learning via orthogonal projections onto capsule subspaces
Liheng Zhang, Marzieh Edraki, and Guo-Jun Qi · 2018
Cited alongside, same era.
Star-caps: Capsule networks with straight-through attentive routing
Karim Ahmed and Lorenzo Torresani · 2019
Cited alongside, same era.
Self-routing capsule networks
Taeyoung Hahn, Myeongjang Pyeon, and Gunhee Kim · 2019
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner
Cited in the paper.
Efficient joint gradient based attack against sor defense for 3d point cloud classification
Chengcheng Ma, Weiliang Meng, Baoyuan Wu, Shibiao Xu, and Xiaopeng Zhang · 2020
Later among the works it cites.
Detecting and diagnosing adversarial images with class-conditional capsule reconstructions
Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison Cottrell, and Geoffrey Hinton · 2020
Later among the works it cites.
Capsule routing via variational bayes
Fabio De Sousa Ribeiro, Georgios Leontidis, and Stefanos D Kollias · 2020
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Capsules with inverted dot-product attention routing
Yao-Hung Hubert Tsai, Nitish Srivastava, Hanlin Goh, and Ruslan Salakhutdinov · 2020
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Intriguing properties of adversarial training at scale
Cihang Xie and Alan Loddon Yuille · 2020
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Interpretable graph capsule networks for object recognition
Jindong Gu and Volker Tresp · 2021
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