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The field of few-shot learning (FSL) has shown promising results in scenarios where training data is limited, but its vulnerability to backdoor attacks remains largely unexplored.
Defensive Few-shot Adversarial Learning
Li, W.; Wang, L.; Zhang, X.; Huo, J.; Gao, Y.; and Luo, J. 2019b · 1911
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Label-consistent backdoor attacks
Turner, A.; Tsipras, D.; and Madry, A. 2019 · 1912
Earlier work this paper cites.
Self-supervised knowledge distillation for few-shot learning
Rajasegaran, J.; Khan, S.; Hayat, M.; Khan, F. S.; and Shah, M. 2020 · 2006
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Siamese neural networks for one-shot image recognition
Koch, G.; Zemel, R.; Salakhutdinov, R.; et al. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Matching networks for one shot learning
Vinyals, O.; Blundell, C.; Lillicrap, T.; Wierstra, D.; et al. 2016 · 2016
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A survey of transfer learning
Weiss, K.; Khoshgoftaar, T. M.; and Wang, D. 2016 · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X.; Liu, C.; Li, B.; Lu, K.; and Song, D. 2017 · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
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Neural trojans
Liu, Y.; Xie, Y.; and Srivastava, A. 2017 · 2017
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Prototypical networks for few-shot learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
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Meta-learning with differentiable closed-form solvers
Bertinetto, L.; Henriques, J. F.; Torr, P. H.; and Vedaldi, A. 2018 · 2018
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Versa: Versatile and efficient few-shot learning
Gordon, J.; Bronskill, J.; Bauer, M.; Nowozin, S.; and Turner, R. E. 2018 · 2018
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Recent advances in convolutional neural networks
Gu, J.; Wang, Z.; Kuen, J.; Ma, L.; Shahroudy, A.; Shuai, B.; Liu, T.; Wang, X.; Wang, G.; Cai, J.; et al. 2018 · 2018
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Fine-pruning: Defending against backdooring attacks on deep neural networks
Liu, K.; Dolan-Gavitt, B.; and Garg, S. 2018 · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
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Meta-learning for semi-supervised few-shot classification
Ren, M.; Triantafillou, E.; Ravi, S.; Snell, J.; Swersky, K.; Tenenbaum, J. B.; Larochelle, H.; and Zemel, R. S. 2018 · 2018
Earlier work this paper cites.
Learning to compare: Relation network for few-shot learning
Sung, F.; Yang, Y.; Zhang, L.; Xiang, T.; Torr, P. H.; and Hospedales, T. M. 2018 · 2018
Cited alongside, same era.
A closer look at few-shot classification
Chen, W.-Y.; Liu, Y.-C.; Kira, Z.; Wang, Y.-C. F.; and Huang, J.-B. 2019 · 2019
Cited alongside, same era.
A baseline for few-shot image classification
Dhillon, G. S.; Chaudhari, P.; Ravichandran, A.; and Soatto, S. 2019 · 2019
Cited alongside, same era.
Badnets: Evaluating backdooring attacks on deep neural networks
Gu, T.; Liu, K.; Dolan-Gavitt, B.; and Garg, S. 2019 · 2019
Cited alongside, same era.
Meta-learning with differentiable convex optimization
Lee, K.; Maji, S.; Ravichandran, A.; and Soatto, S. 2019 · 2019
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Wang, B.; Yao, Y.; Shan, S.; Li, H.; Viswanath, B.; Zheng, H.; and Zhao, B. Y. 2019 · 2019
AdvFilter: predictive perturbation-aware filtering against adversarial attack via multi-domain learning
Huang, Y.; Guo, Q.; Juefei-Xu, F.; Ma, L.; Miao, W.; Liu, Y.; and Pu, G. 2021 · 2021
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Relational Embedding for Few-Shot Classification
Kang, D.; Kwon, H.; Min, J.; and Cho, M. 2021 · 2021
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LibFewShot: A Comprehensive Library for Few-shot Learning
Li, W.; Dong, C.; Tian, P.; Qin, T.; Yang, X.; Wang, Z.; Huo, J.; Shi, Y.; Wang, L.; Gao, Y.; et al. 2021 · 2021
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WaNet–Imperceptible Warping-based Backdoor Attack
Nguyen, A.; and Tran, A. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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Cited alongside, same era.
Backdoor attacks on federated meta-learning
Chen, C.-L.; Golubchik, L.; and Paolieri, M. 2020 · 2020
Cited alongside, same era.
Crosstransformers: spatially-aware few-shot transfer
Doersch, C.; Gupta, A.; and Zisserman, A. 2020 · 2020
Cited alongside, same era.
Adv-watermark: A novel watermark perturbation for adversarial examples
Jia, X.; Wei, X.; Cao, X.; and Han, X. 2020 · 2020
Cited alongside, same era.
Input-aware dynamic backdoor attack
Nguyen, T. A.; and Tran, A. 2020 · 2020
Cited alongside, same era.
Rapid learning or feature reuse? towards understanding the effectiveness of maml
Raghu, A.; Raghu, M.; Bengio, S.; and Vinyals, O. 2020 · 2020
Cited alongside, same era.
Hidden trigger backdoor attacks
Saha, A.; Subramanya, A.; and Pirsiavash, H. 2020 · 2020
Cited alongside, same era.
Few-shot classification with feature map reconstruction networks
Wertheimer, D.; Tang, L.; and Hariharan, B. 2021 · 2021
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Free lunch for few-shot learning: Distribution calibration
Yang, S.; Liu, L.; and Xu, M. 2021 · 2021
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SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness
Gu, J.; Zhao, H.; Tresp, V.; and Torr, P. H. 2022 · 2022
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Few-shot backdoor defense using shapley estimation
Guan, J.; Tu, Z.; He, R.; and Tao, D. 2022 · 2022
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A large-scale multiple-objective method for black-box attack against object detection
Liang, S.; Li, L.; Fan, Y.; Jia, X.; Li, J.; Wu, B.; and Cao, X. 2022 · 2022
Later among the works it cites.
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
Liu, X.; Liu, J.; Bai, Y.; Gu, J.; Chen, T.; Jia, X.; and Cao, X. 2022 · 2022
Later among the works it cites.
Backdoor Defense via Adaptively Splitting Poisoned Dataset
Gao, K.; Bai, Y.; Gu, J.; Yang, Y.; and Xia, S.-T. 2023 · 2023
Closest in time.
Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks
Gu, J.; Wei, F.; Torr, P.; and Hu, H. 2023 · 2023
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Generating transferable 3d adversarial point cloud via random perturbation factorization
He, B.; Liu, J.; Li, Y.; Liang, S.; Li, J.; Jia, X.; and Cao, X. 2023 · 2023
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ALA: Naturalness-aware Adversarial Lightness Attack
Huang, Y.; Sun, L.; Guo, Q.; Juefei-Xu, F.; Zhu, J.; Feng, J.; Liu, Y.; and Pu, G. 2023 · 2023
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BackdoorBox: A python toolbox for backdoor learning
Li, Y.; Ya, M.; Bai, Y.; Jiang, Y.; and Xia, S.-T. 2023 · 2023
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