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Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to the backdoor attacks, which leads to malicious behaviors of DNNs when specific triggers are attached to the input images.
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Krizhevsky, A · 2009
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He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Ruder, S · 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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Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G · 2018
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Trojaning attack on neural networks
Liu, Y., Ma, S., Aafer, Y., Lee, W., Zhai, J., Wang, W., and Zhang, X · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Ensemble adversarial training: Attacks and defenses
Tramèr, F., Boneh, D., Kurakin, A., Goodfellow, I., Papernot, N., and McDaniel, P · 2018
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Spectral signatures in backdoor attacks
Tran, B., Li, J., and Madry, A · 2018
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Robust anomaly detection and backdoor attack detection via differential privacy
Du, M., Jia, R., and Song, D · 2019
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Maxup: A simple way to improve generalization of neural network training
Gong, C., Ren, T., Ye, M., and Liu, Q · 2020
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Input-aware dynamic backdoor attack
Nguyen, T. A. and Tran, A · 2020
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Tbt: Targeted neural network attack with bit trojan
Rakin, A. S., He, Z., and Fan, D · 2020
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Certified robustness to label-flipping attacks via randomized smoothing
Rosenfeld, E., Winston, E., Ravikumar, P., and Kolter, Z · 2020
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Defending against backdoor attack on deep neural networks
Xu, K., Liu, S., Chen, P.-Y., Zhao, P., and Lin, X · 2020
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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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Badnets: Evaluating backdooring attacks on deep neural networks
Gu, T., Liu, K., Dolan-Gavitt, B., and Garg, S · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Label-consistent backdoor attacks
Turner, A., Tsipras, D., and Madry, A · 2019
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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
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Bridging mode connectivity in loss landscapes and adversarial robustness
Zhao, P., Chen, P.-Y., Das, P., Ramamurthy, K. N., and Lin, X · 2019
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Xue, M., He, C., Wang, J., and Liu, W · 2020
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Disabling backdoor and identifying poison data by using knowledge distillation in backdoor attacks on deep neural networks
Yoshida, K. and Fujino, T · 2020
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Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff
Borgnia, E., Cherepanova, V., Fowl, L., Ghiasi, A., Geiping, J., Goldblum, M., Goldstein, T., and Gupta, A · 2021
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Spectre: defending against backdoor attacks using robust statistics
Hayase, J., Kong, W., Somani, R., and Oh, S · 2021
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Backdoor defense via decoupling the training process
Huang, K., Li, Y., Wu, B., Qin, Z., and Ren, K · 2021
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Invisible backdoor attack with sample-specific triggers
Li, Y., Li, Y., Wu, B., Li, L., He, R., and Lyu, S · 2021
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Wanet–imperceptible warping-based backdoor attack
Nguyen, A. and Tran, A · 2021
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Adversarial neuron pruning purifies backdoored deep models
Wu, D. and Wang, Y · 2021
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