Fetching the paper…
Reading the bibliography…
It is widely recognized that deep learning models lack robustness to adversarial examples.
The nature of statistical learning theory
Vladimir Vapnik · 1999
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
Earlier work this paper cites.
Torchvision the machine-vision package of torch
Sébastien Marcel and Yann Rodriguez · 2010
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 learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Improved svrg for non-strongly-convex or sum-of-non-convex objectives
Zeyuan Allen-Zhu and Yang Yuan · 2016
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.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Low-rank solutions of linear matrix equations via procrustes flow
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, and Ben Recht · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Earlier work this paper cites.
Towards interpretable deep neural networks by leveraging adversarial examples
Yinpeng Dong, Hang Su, Jun Zhu, and Fan Bao · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 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.
Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
Earlier work this paper cites.
Towards understanding generalization of deep learning: Perspective of loss landscapes
Lei Wu, Zhanxing Zhu, et al · 2017
Earlier work this paper cites.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Earlier work this paper cites.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2018
Cited alongside, same era.
Deep linear networks with arbitrary loss: All local minima are global
Thomas Laurent and James Brecht · 2018
Cited alongside, same era.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
Nesterov accelerated gradient and scale invariance for adversarial attacks
Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft · 2020
Later among the works it cites.
A self-supervised approach for adversarial robustness
Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli · 2020
Later among the works it cites.
Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
Later among the works it cites.
Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
Later among the works it cites.
Normalized flat minima: Exploring scale invariant definition of flat minima for neural networks using pac-bayesian analysis
Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama · 2020
Later among the works it cites.
Fast is better than free: Revisiting adversarial training
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 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.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Cited alongside, same era.
Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
Cited alongside, same era.
Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
Cited alongside, same era.
Eric Wong, Leslie Rice, and J Zico Kolter · 2020
Later among the works it cites.
Tensorflow model garden. 2020
Hongkun Yu, Chen Chen, Xianzhi Du, Yeqing Li, Abdullah Rashwan, Le Hou, Pengchong Jin, Fan Yang, Frederick Liu, Jaeyoun Kim, et al · 2020
Later among the works it cites.
Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2021
Later among the works it cites.
Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks
Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
On generating transferable targeted perturbations
Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
Enhancing the transferability of adversarial attacks through variance tuning
Xiaosen Wang and Kun He · 2021
Later among the works it cites.
Boosting adversarial transferability through enhanced momentum
Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, and Kun He · 2021
Later among the works it cites.
Trs: Transferability reduced ensemble via promoting gradient diversity and model smoothness
Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin Rubinstein, Ce Zhang, and Bo Li · 2021
Later among the works it cites.
Frequency domain model augmentation for adversarial attack
Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xianglong Liu, Jian Zhang, and Jingkuan Song · 2022
Later among the works it cites.
Diffusion models for adversarial purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Animashree Anandkumar · 2022
Later among the works it cites.
Boosting the transferability of adversarial attacks with reverse adversarial perturbation
Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu · 2022
Later among the works it cites.
Maxvit: Multi-axis vision transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li · 2022
Later among the works it cites.
Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability
Yifeng Xiong, Jiadong Lin, Min Zhang, John E Hopcroft, and Kun He · 2022
Later among the works it cites.
Bootstrap generalization ability from loss landscape perspective
Huanran Chen, Shitong Shao, Ziyi Wang, Zirui Shang, Jin Chen, Xiaofeng Ji, and Xinxiao Wu · 2023
Closest in time.
A light recipe to train robust vision transformers
Edoardo Debenedetti, Vikash Sehwag, and Prateek Mittal · 2023
Closest in time.
How robust is google’s bard to adversarial image attacks?
Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, and Jun Zhu · 2023
Closest in time.
T-sea: Transfer-based self-ensemble attack on object detection
Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, and Kevin Zhang · 2023
Closest in time.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
Closest in time.
To make yourself invisible with adversarial semantic contours
Yichi Zhang, Zijian Zhu, Hang Su, Jun Zhu, Shibao Zheng, Yuan He, and Hui Xue · 2023
Closest in time.