Fetching the paper…
Reading the bibliography…
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs).
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Once-for-all adversarial training: In-situ tradeoff between robustness and accuracy for free
Haotao Wang, Tianlong Chen, Shupeng Gui, Ting-Kuei Hu, Ji Liu, and Zhangyang Wang · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Adversarial examples in the physical world, 2016
Alexey Kurakin, Ian Goodfellow, Samy Bengio, et al · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Earlier work this paper cites.
A discriminative feature learning approach for deep face recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao · 2016
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
Earlier work this paper cites.
Residual convolutional ctc networks for automatic speech recognition
Yisen Wang, Xuejiao Deng, Songbai Pu, and Zhiheng Huang · 2017
Cited alongside, same era.
Compression to the rescue: Defending from adversarial attacks across modalities
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E Kounavis, and Duen Horng Chau · 2018
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Guneet S Dhillon, Kamyar Azizzadenesheli, Zachary C Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Anima Anandkumar · 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.
Improved robustness to adversarial examples using lipschitz regularization of the loss
Chris Finlay, Adam Oberman, and Bilal Abbasi · 2018
Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks
Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, and Boqing Gong · 2019
Later among the works it cites.
Adversarial defense by restricting the hidden space of deep neural networks
Aamir Mustafa, Salman Khan, Munawar Hayat, Roland Goecke, Jianbing Shen, and Ling Shao · 2019
Later among the works it cites.
Harnessing the vulnerability of latent layers in adversarially trained models
Abhishek Sinha, Vineeth N Balasubramanian, Harshitha Machiraju, et al · 2019
Later among the works it cites.
On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
Later among the works it cites.
Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey · 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.
Adnan Siraj Rakin, Jinfeng Yi, Boqing Gong, and Deliang Fan · 2018
Cited alongside, same era.
Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2019
Cited alongside, same era.
Hilbert-based generative defense for adversarial examples
Yang Bai, Yan Feng, Yisen Wang, Tao Dai, Shu-Tao Xia, and Yong Jiang · 2019
Cited alongside, same era.
Kaidi Xu, Sijia Liu, Gaoyuan Zhang, Mengshu Sun, Pu Zhao, Quanfu Fan, Chuang Gan, and Xue Lin · 2019
Later among the works it cites.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
Later among the works it cites.
Adversarial camouflage: Hiding physical-world attacks with natural styles
Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A Kai Qin, and Yun Yang · 2020
Later among the works it cites.
Imbalanced gradients: A new cause of overestimated adversarial robustness
Linxi Jiang, Xingjun Ma, Zejia Weng, James Bailey, and Yu-Gang Jiang · 2020
Later among the works it cites.
Adversarial neural pruning with latent vulnerability suppression
Divyam Madaan and Sung Ju Hwang · 2020
Later among the works it cites.
Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and J Zico Kolter · 2020
Later among the works it cites.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
Later among the works it cites.
Resisting adversarial attacks by k k -winners-take-all
Chang Xiao, Peilin Zhong, and Changxi Zheng · 2020
Later among the works it cites.
Understanding adversarial attacks on deep learning based medical image analysis systems
Xingjun Ma, Yuhao Niu, Lin Gu, Yisen Wang, Yitian Zhao, James Bailey, and Feng Lu · 2021
Closest in time.
A unified approach to interpreting and boosting adversarial transferability
Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, and Quanshi Zhang · 2021
Closest in time.