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Intuitively, a backdoor attack against Deep Neural Networks (DNNs) is to inject hidden malicious behaviors into DNNs such that the backdoor model behaves legitimately for benign inputs, yet invokes a predefined malicious behavior when its input contains a malicious trigger.
Binwalk: Firmware analysis tool
Heffner, C · 2010
Earlier work this paper cites.
Stuxnet: Dissecting a cyberwarfare weapon
Langner, R · 2011
Earlier work this paper cites.
Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Désidéri, J.-A · 2012
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
The art of memory forensics: detecting malware and threats in windows, linux, and Mac memory
Ligh, M. H., Case, A., Levy, J., and Walters, A · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mirza, M., and Osindero, S · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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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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Trojaning attack on neural networks
Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., and Zhang, X · 2017
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Universal adversarial perturbations
Moosavi-Dezfooli, S., Fawzi, A., Fawzi, O., and Frossard, P · 2017
Earlier work this paper cites.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Earlier work this paper cites.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Adi, Y., Baum, C., Cissé, M., Pinkas, B., and Keshet, J · 2018
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Generating natural language adversarial examples
Alzantot, M., Sharma, Y., Elgohary, A., Ho, B., Srivastava, M. B., and Chang, K · 2018
Earlier work this paper cites.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Liu, K., Dolan-Gavitt, B., and Garg, S · 2018
Earlier work this paper cites.
Trojaning attack on neural networks
Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., and Zhang, X · 2018
Earlier work this paper cites.
Resilience of pruned neural network against poisoning attack
Zhao, B., and Lao, Y · 2018
Earlier work this paper cites.
A new backdoor attack in cnns by training set corruption without label poisoning
Barni, M., Kallas, K., and Tondi, B · 2019
Cited alongside, same era.
A backdoor attack against LSTM-Based text classification systems
Dai, J., Chen, C., and Li, Y · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2019
Cited alongside, same era.
Persistent and unforgeable watermarks for deep neural networks
Li, H., Willson, E., Zheng, H., and Zhao, B. Y · 2019
Cited alongside, same era.
Abs: Scanning neural networks for back-doors by artificial brain stimulation
Invisible backdoor attacks on deep neural networks via steganography and regularization
Li, S., Xue, M., Zhao, B., Zhu, H., and Zhang, X · 2020
Closest in time.
Composite backdoor attack for deep neural network by mixing existing benign features
Lin, J., Xu, L., Liu, Y., and Zhang, X · 2020
Closest in time.
A survey on neural trojans
Liu, Y., Mondal, A., Chakraborty, A., Zuzak, M., Jacobsen, N., Xing, D., and Srivastava, A · 2020
Closest in time.
Hidden trigger backdoor attacks
Saha, A., Subramanya, A., and Pirsiavash, H · 2020
Closest in time.
Dynamic backdoor attacks against machine learning models
Salem, A., Wen, R., Backes, M., Ma, S., and Zhang, Y · 2020
Closest in time.
Universal adversarial training
Shafahi, A., Najibi, M., Xu, Z., Dickerson, J. P., Davis, L. S., and Goldstein, T · 2020
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Liu, Y., Lee, W.-C., Tao, G., Ma, S., Aafer, Y., and Zhang, X · 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
Cited alongside, same era.
DBA: Distributed backdoor attacks against federated learning
Xie, C., Huang, K., Chen, P.-Y., and Li, B · 2019
Cited alongside, same era.
Design of intentional backdoors in sequential models
Yang, Z., Iyer, N., Reimann, J., and Virani, N · 2019
Cited alongside, same era.
Blind backdoors in deep learning models
Bagdasaryan, E., and Shmatikov, V · 2020
Cited alongside, same era.
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2020
Cited alongside, same era.
BadNL: Backdoor attacks against nlp models
Chen, X., Salem, A., Backes, M., Ma, S., and Zhang, Y · 2020
Cited alongside, same era.
Closest in time.
Gotta catch ’em all: Using honeypots to catch adversarial attacks on neural networks
Shan, S., Wenger, E., Wang, B., Li, B., Zheng, H., and Zhao, B. Y · 2020
Closest in time.
Gotta catch’em all: Using honeypots to catch adversarial attacks on neural networks
Shan, S., Wenger, E., Wang, B., Li, B., Zheng, H., and Zhao, B. Y · 2020
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Bypassing backdoor detection algorithms in deep learning
Tan, T. J. L., and Shokri, R · 2020
Closest in time.
Wang, Y., Sarkar, E., Maniatakos, M., and Jabari, S. E · 2020
Closest in time.
Backdoor attacks on facial recognition in the physical world
Wenger, E., Passananti, J., Yao, Y., Zheng, H., and Zhao, B. Y · 2020
Closest in time.
Revealing backdoors, post-training, in DNN classifiers via novel inference on optimized perturbations inducing group misclassification
Xiang, Z., Miller, D. J., and Kesidis, G · 2020
Closest in time.
Backdoor attacks to graph neural networks
Zhang, Z., Jia, J., Wang, B., and Gong, N. Z · 2020
Closest in time.
Backdoor embedding in convolutional neural network models via invisible perturbation
Zhong, H., Liao, C., Squicciarini, A. C., Zhu, S., and Miller, D. J · 2020
Closest in time.
Graph backdoor
Xi, Z., Pang, R., Ji, S., and Wang, T · 2021
Closest in time.
Latent backdoor attacks on deep neural networks
Yao, Y., Li, H., Zheng, H., and Zhao, B. Y · 2055
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