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Backdoor (Trojan) attack is a common threat to deep neural networks, where samples from one or more source classes embedded with a backdoor trigger will be misclassified to adversarial target classes.
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Krizhevsky, A · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
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Adam: A method for stochastic optimization
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 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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Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2017
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Detecting backdoor attacks on deep neural networks by activation clustering
Chen, B., Carvalho, W., Baracaldo, N., Ludwig, H., Edwards, B., Lee, T., Molloy, I., and Srivastava, B · 2018
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GTSRB Leaderboard
Leaderboard · 2018
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Fine-pruning: Defending against backdoor attacks on deep neural networks
Liu, K., Doan-Gavitt, B., and Garg, S · 2018
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Spectral signatures in backdoor attacks
Tran, B., Li, J., and Madry, A · 2018
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Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks
Chen, H., Fu, C., Zhao, J., and Koushanfar, F · 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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Badnets: Evaluating backdooring attacks on deep neural networks
Gu, T., Liu, K., Dolan-Gavitt, B., and Garg, S · 2019
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TABOR: A highly accurate approach to inspecting and restoring Trojan backdoors in AI systems
Guo, W., Wang, L., Xing, X., Du, M., and Song, D · 2019
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ABS: Scanning neural networks for back-doors by artificial brain stimulation
Liu, Y., Lee, W., Tao, G., Ma, S., Aafer, Y., and Zhang, X · 2019
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Clean-label 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 · 2019
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A benchmark study of backdoor data poisoning defenses for deep neural network classifiers and a novel defense
Xiang, Z., Miller, D., and Kesidis, G · 2019
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Latent backdoor attacks on deep neural networks
Yao, Y., Li, H., Zheng, H., and Zhao, B. Y · 2019
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Hidden trigger backdoor attacks
A. Saha, A. Subramanya, H. P · 2020
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Wanet - imperceptible warping-based backdoor attack
Nguyen, A. and Tran, A · 2021
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Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
Shen, G., Liu, Y., Tao, G., An, S., Xu, Q., Cheng, S., Ma, S., and Zhang, X · 2021
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Adversarial neuron pruning purifies backdoored deep models
Wu, D. and Wang, Y · 2021
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L-RED: Efficient post-training detection of imperceptible backdoor attacks without access to the training set
Xiang, Z., Miller, D. J., and Kesidis, G · 2021
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Detecting AI Trojans using meta neural analysis
Xu, X., Wang, Q., Li, H., Borisov, N., Gunter, C., and Li, B · 2021
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Few-shot backdoor defense using shapley estimation
Guan, J., Tu, Z., He, R., and Tao, D · 2022
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Sentinet: Detecting localized universal attacks against deep learning systems
Chou, E., Tramèr, F., Pellegrino, G., and Boneh, D · 2020
Cited alongside, same era.
Februus: Input purification defense against trojan attacks on deep neural network systems
Doan, B. G., Abbasnejad, E., and C.Ranasinghe, D · 2020
Cited alongside, same era.
Robust anomaly detection and backdoor attack detection via differential privacy
Du, M., Jia, R., and Song, D · 2020
Cited alongside, same era.
Universal litmus patterns: Revealing backdoor attacks in cnns
Kolouri, S., Saha, A., Pirsiavash, H., and Hoffmann, H · 2020
Cited alongside, same era.
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Liu, Y., Ma, X., Bailey, J., and Lu, F · 2020
Cited alongside, same era.
Adversarial learning in statistical classification: A comprehensive review of defenses against attacks
Miller, D. J., Xiang, Z., and Kesidis, G · 2020
Cited alongside, same era.
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Trigger hunting with a topological prior for trojan detection
Hu, X., Lin, X., Cogswell, M., Yao, Y., Jha, S., and Chen, C · 2022
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Backdoor defense via decoupling the training process
Huang, K., Li, Y., Wu, B., Qin, Z., and Ren, K · 2022
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IEEE Trojan Removal Competition
ICLR · 2022
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BadEncoder: Backdoor attacks to pre-trained encoders in self-supervised learning
Jia, J., Liu, Y., and Gong, N. Z · 2022
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Trojan Detection Challenge NeurIPS 2022
NeurIPS · 2022
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Label-Smoothed Backdoor Attack
Peng, M., Xiong, Z., Sun, M., and Li, P · 2022
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Better trigger inversion optimization in backdoor scanning
Tao, G., Shen, G., Liu, Y., An, S., Xu, Q., Ma, S., Li, P., and Zhang, X · 2022
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Bppattack: Stealthy and efficient trojan attacks against deep neural networks via image quantization and contrastive adversarial learning
Wang, Z., Zhai, J., and Ma, S · 2022
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Minimum excess risk in bayesian learning
Xu, A. and Raginsky, M · 2022
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Adversarial unlearning of backdoors via implicit hypergradient
Zeng, Y., Chen, S., Park, W., Mao, Z., Jin, M., and Jia, R · 2022
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Defeat: Deep hidden feature backdoor attacks by imperceptible perturbation and latent representation constraints
Zhao, Z., Chen, X., Xuan, Y., Dong, Y., Wang, D., and Liang, K · 2022
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Data-free backdoor removal based on channel lipschitzness
Zheng, R., Tang, R., Li, J., and Liu, L · 2022
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UNICORN: A unified backdoor trigger inversion framework
Wang, Z., Mei, K., Zhai, J., and Ma, S · 2023
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