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Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention.
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Wei, X., Liang, S., Chen, N., Cao, X.: Transferable adversarial attacks for image and video object detection. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. pp. 954–960 (2019)
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Andriushchenko, M., Croce, F., Flammarion, N., Hein, M.: Square attack: A query-efficient black-box adversarial attack via random search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J. (eds.) ECCV. Lecture Notes in Computer Science, vol. 12368, pp. 484–501. Springer (2020). https://doi.org/10.1007/978-3-030-58592-1_29, https://doi.org/10.1007/978-3-030-58592-1\_29
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Andriushchenko, M., Flammarion, N.: Understanding and improving fast adversarial training. NeurIPS (2020)
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Croce, F., Hein, M.: Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In: ICML (2020)
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Mu, N., Wagner, D.: Defending against adversarial patches with robust self-attention. In: ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning (2021)
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Liang, S., Wei, X., Yao, S., Cao, X.: Efficient adversarial attacks for visual object tracking. In: European Conference on Computer Vision. pp. 34–50. Springer (2020)
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S., V.B., Babu, R.V.: Single-step adversarial training with dropout scheduling. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020. pp. 947–956. Computer Vision Foundation / IEEE (2020). https://doi.org/10.1109/CVPR42600.2020.00103, https://openaccess.thecvf.com/content\_CVPR\_2020/html/B.S.\_Single-Step\_Adversarial\_Training\_With\_Dropout\_Scheduling\_CVPR\_2020\_paper.html
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Vivek, B., Babu, R.V.: Single-step adversarial training with dropout scheduling. In: CVPR (2020)
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Bai, Y., Mei, J., Yuille, A., Xie, C.: Are transformers more robust than CNNs? In: Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems (2021), https://openreview.net/forum?id=hbHkvGBZB9
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Paul, S., Chen, P.: Vision transformers are robust learners. CoRR abs/2105.07581
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Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, ICML. vol. 139, pp. 10347–10357 (2021)
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2022
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Fu, Y., Zhang, S., Wu, S., Wan, C., Lin, Y.: Patch-fool: Are vision transformers always robust against adversarial perturbations? In: International Conference on Learning Representations (2022)
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Jia, X., Zhang, Y., Wu, B., Ma, K., Wang, J., Cao, X.: Las-at: Adversarial training with learnable attack strategy. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 13398–13408 (June 2022)
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Jia, X., Zhang, Y., Wu, B., Wang, J., Cao, X.: Boosting fast adversarial training with learnable adversarial initialization. IEEE Transactions on Image Processing (2022)
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Wang, W., Yao, L., Chen, L., Lin, B., Cai, D., He, X., Liu, W.: Crossformer: A versatile vision transformer hinging on cross-scale attention. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=_PHymLIxuI
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Wang, Z., Jiang, W., Zhu, Y., Yuan, L., Song, Y., Liu, W.: Dynamixer: A vision MLP architecture with dynamic mixing. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvári, C., Niu, G., Sabato, S. (eds.) International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research, vol. 162, pp. 22691–22701. PMLR (2022), https://proceedings.mlr.press/v162/wang22i.html
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