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Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into models remain active indefinitely.
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Convex Optimization
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Krizhevsky, A. et al · 2009
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A theory of learning from different domains
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Making gradient descent optimal for strongly convex stochastic optimization
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Poisoning attacks against support vector machines
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Batch-incremental versus instance-incremental learning in dynamic and evolving data
Read, J., Bifet, A., Pfahringer, B., and Holmes, G · 2012
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Adversarial label flips attack on support vector machines
Xiao, H., Xiao, H., and Eckert, C · 2012
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Libol: A library for online learning algorithms
Hoi, S. C., Wang, J., and Zhao, P · 2014
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How transferable are features in deep neural networks?
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V · 2015
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Visual domain adaptation: A survey of recent advances
Patel, V. M., Gopalan, R., Li, R., and Chellappa, R · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Firecaffe: near-linear acceleration of deep neural network training on compute clusters
Iandola, F. N., Moskewicz, M. W., Ashraf, K., and Keutzer, K · 2016
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Convolutional neural networks for medical image analysis: Full training or fine tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J · 2016
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A survey of transfer learning
Weiss, K., Khoshgoftaar, T. M., and Wang, D · 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
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Certified defenses for data poisoning attacks
Steinhardt, J., Koh, P. W. W., and Liang, P. S · 2017
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Universal language model fine-tuning for text classification
Howard, J. and Ruder, S · 2018
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On the location of the minimizer of the sum of two strongly convex functions
Kuwaranancharoen, K. and Sundaram, S · 2018
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Online deep learning: learning deep neural networks on the fly
Sahoo, D., Pham, Q., Lu, J., and Hoi, S. C · 2018
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Learning from synthetic data: Addressing domain shift for semantic segmentation
Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S. N., and Chellappa, R · 2018
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Spectral signatures in backdoor attacks
Tran, B., Li, J., and Madry, A · 2018
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Composite backdoor attack for deep neural network by mixing existing benign features
Lin, J., Xu, L., Liu, Y., and Zhang, X · 2020
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Reflection backdoor: A natural backdoor attack on deep neural networks
Liu, Y., Ma, X., Bailey, J., and Lu, F · 2020
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Input-aware dynamic backdoor attack
Nguyen, T. A. and Tran, A · 2020
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A tale of evil twins: Adversarial inputs versus poisoned models
Pang, R., Shen, H., Zhang, X., Ji, S., Vorobeychik, Y., Luo, X., Liu, A., and Wang, T · 2020
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An embarrassingly simple approach for trojan attack in deep neural networks
Tang, R., Du, M., Liu, N., Yang, F., and Hu, X · 2020
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Deep visual domain adaptation: A survey
Wang, M. and Deng, W · 2018
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Data poisoning attacks against online learning
Wang, Y. and Chaudhuri, K · 2018
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Lifelong learning with dynamically expandable networks
Yoon, J., Yang, E., Lee, J., and Hwang, S. J · 2018
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Analyzing federated learning through an adversarial lens
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S · 2019
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Strip: A defence against trojan attacks on deep neural networks
Gao, Y., Xu, C., Wang, D., Chen, S., Ranasinghe, D. C., and Nepal, S · 2019
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Abs: Scanning neural networks for back-doors by artificial brain stimulation
Liu, Y., Lee, W.-C., Tao, G., Ma, S., Aafer, Y., and Zhang, X · 2019
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Veldanda, A. and Garg, S · 2020
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Attack of the tails: Yes, you really can backdoor federated learning
Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-y., Lee, K., and Papailiopoulos, D · 2020
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Yin, D., Farajtabar, M., Li, A., Levine, N., and Mott, A · 2020
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How to retrain recommender system? a sequential meta-learning method
Zhang, Y., Feng, F., Wang, C., He, X., Wang, M., Li, Y., and Zhang, Y · 2020
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A comprehensive survey on transfer learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q · 2020
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Active online learning with hidden shifting domains
Chen, Y., Luo, H., Ma, T., and Zhang, C · 2021
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Online learning: A comprehensive survey
Hoi, S. C., Sahoo, D., Lu, J., and Zhao, P · 2021
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Cycle self-training for domain adaptation
Liu, H., Wang, J., and Long, M · 2021
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Wanet-imperceptible warping-based backdoor attack
Nguyen, A. and Tran, A · 2021
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Accumulative poisoning attacks on real-time data
Pang, T., Yang, X., Dong, Y., Su, H., and Zhu, J · 2021
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Backdoor attacks against deep learning systems in the physical world
Wenger, E., Passananti, J., Bhagoji, A. N., Yao, Y., Zheng, H., and Zhao, B. Y · 2021
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Datamodels: Predicting predictions from training data
Ilyas, A., Park, S. M., Engstrom, L., Leclerc, G., and Madry, A · 2022
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