Fetching the paper…
Reading the bibliography…
Vision transformers (ViTs) have demonstrated impressive performance on a series of computer vision tasks, yet they still suffer from adversarial examples.
Nesterov accelerated gradient and scale invariance for adversarial attacks
Lin, J.; Song, C.; He, K.; Wang, L.; and Hopcroft, J. E. 2019 · 1908
Earlier work this paper cites.
A Value for n-person Games
Shapley, L. 1988 · 1988
Earlier work this paper cites.
Sparse black-box video attack with reinforcement learning
Yan, H.; Wei, X.; and Li, B. 2020 · 2001
Earlier work this paper cites.
Skip connections matter: On the transferability of adversarial examples generated with resnets
Wu, D.; Wang, Y.; Xia, S.-T.; Bailey, J.; and Ma, X. 2020a · 2002
Earlier work this paper cites.
Random Forests
Breiman, L. 2004 · 2004
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
Earlier work this paper cites.
A unified approach to interpreting and boosting adversarial transferability
Wang, X.; Ren, J.; Lin, S.; Zhu, X.; Wang, Y.; and Zhang, Q. 2020 · 2010
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E.; Srivastava, N.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2012 · 2012
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2015 · 2015
Earlier work this paper cites.
Identity mappings in deep residual networks
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2016 · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Kurakin, A.; Goodfellow, I.; Bengio, S.; et al. 2016 · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Liu, Y.; Chen, X.; Liu, C.; and Song, D. 2016 · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 2016 · 2016
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; and Batra, D. 2017 · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C.; Ioffe, S.; Vanhoucke, V.; and Alemi, A. A. 2017 · 2017
Cited alongside, same era.
Convit: Improving vision transformers with soft convolutional inductive biases
d’Ascoli, S.; Touvron, H.; Leavitt, M.; Morcos, A.; Biroli, G.; and Sagun, L. 2021 · 2021
Closest in time.
LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference
Graham, B.; El-Nouby, A.; Touvron, H.; Stock, P.; Joulin, A.; Jégou, H.; and Douze, M. 2021 · 2021
Closest in time.
Han, K.; Xiao, A.; Wu, E.; Guo, J.; Xu, C.; and Wang, Y. 2021 · 2021
Closest in time.
Rethinking spatial dimensions of vision transformers
Heo, B.; Yun, S.; Han, D.; Chun, S.; Choe, J.; and Oh, S. J. 2021 · 2021
Closest in time.
On Improving Adversarial Transferability of Vision Transformers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Vaswani, A.; Shazeer, N. M.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Boosting adversarial attacks with momentum
Dong, Y.; Liao, F.; Pang, T.; Su, H.; Zhu, J.; Hu, X.; and Li, J. 2018 · 2018
Cited alongside, same era.
Transferable adversarial perturbations
Zhou, W.; Hou, X.; Chen, Y.; Tang, M.; Huang, X.; Gan, X.; and Yang, Y. 2018 · 2018
Cited alongside, same era.
Evading defenses to transferable adversarial examples by translation-invariant attacks
Dong, Y.; Pang, T.; Su, H.; and Zhu, J. 2019 · 2019
Cited alongside, same era.
PyTorch Image Models
Wightman, R. 2019 · 2019
Cited alongside, same era.
Improving transferability of adversarial examples with input diversity
Xie, C.; Zhang, Z.; Zhou, Y.; Bai, S.; Wang, J.; Ren, Z.; and Yuille, A. L. 2019 · 2019
Cited alongside, same era.
Heuristic black-box adversarial attacks on video recognition models
Wei, Z.; Chen, J.; Wei, X.; Jiang, L.; Chua, T.-S.; Zhou, F.; and Jiang, Y.-G. 2020 · 2020
Cited alongside, same era.
Naseer, M.; Ranasinghe, K.; Khan, S.; Khan, F. S.; and Porikli, F. 2021 · 2021
Closest in time.
Vision transformers are robust learners
Paul, S.; and Chen, P.-Y. 2021 · 2021
Closest in time.
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
Shi, Y.; and Han, Y. 2021 · 2021
Closest in time.
Robustart: Benchmarking robustness on architecture design and training techniques
Tang, S.; Gong, R.; Wang, Y.; Liu, A.; Wang, J.; Chen, X.; Yu, F.; Liu, X.; Song, D.; Yuille, A.; et al. 2021 · 2021
Closest in time.
Cross-Modal Transferable Adversarial Attacks from Images to Videos
Wei, Z.; Chen, J.; Wu, Z.; and Jiang, Y.-G. 2021 · 2021
Closest in time.
Cvt: Introducing convolutions to vision transformers
Wu, H.; Xiao, B.; Codella, N.; Liu, M.; Dai, X.; Yuan, L.; and Zhang, L. 2021 · 2021
Closest in time.
Tokens-to-token vit: Training vision transformers from scratch on imagenet
Yuan, L.; Chen, Y.; Wang, T.; Yu, W.; Shi, Y.; Jiang, Z.; Tay, F. E.; Feng, J.; and Yan, S. 2021 · 2021
Closest in time.
Deepvit: Towards deeper vision transformer
Zhou, D.; Kang, B.; Jin, X.; Yang, L.; Lian, X.; Jiang, Z.; Hou, Q.; and Feng, J. 2021 · 2021
Closest in time.