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Vision transformers (ViTs) have demonstrated impressive performance and stronger adversarial robustness compared to Convolutional Neural Networks (CNNs).
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R. Strudel, R. Garcia, I. Laptev, and C. Schmid, “Segmenter: Transformer for semantic segmentation,” in
2021
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2021
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2021
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Y. Dong, H. Su, B. Wu, Z. Li, W. Liu, T. Zhang, and J. Zhu, “Efficient decision-based black-box adversarial attacks on face recognition,” in
2019
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T. Brunner, F. Diehl, M. T. Le, and A. Knoll, “Guessing smart: Biased sampling for efficient black-box adversarial attacks,” in
2019
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M. Cheng, S. Singh, P. H. Chen, P.-Y. Chen, S. Liu, and C.-J. Hsieh, “Sign-opt: A query-efficient hard-label adversarial attack,” in
2019
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R. Wightman, “Pytorch image models,”
2019
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J. Su, D. V. Vargas, and K. Sakurai, “One pixel attack for fooling deep neural networks,”
2019
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly
2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in
2020
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2021
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N. Akhtar, M. Jalwana, M. Bennamoun, and A. S. Mian, “Attack to fool and explain deep networks,”
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2021
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S. Paul and P.-Y. Chen, “Vision transformers are robust learners,” 2022
2022
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