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We tackle the convolution neural networks (CNNs) backdoor detection problem by proposing a new representation called one-pixel signature.
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Liu, K., Dolan-Gavitt, B., Garg, S.: Fine-pruning: Defending against backdooring attacks on deep neural networks. In: International Symposium on Research in Attacks, Intrusions and Defenses. pp. 273–294 (2018)
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770–778 (2016)
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OFFICE, U.A.R.: W911nf-19-s-0012. In: U.S. ARMY RESEARCH OFFICE BROAD AGENCY ANNOUNCEMENT FOR TrojAI (2019)
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Qiao, X., Yang, Y., Li, H.: Defending neural backdoors via generative distribution modeling. In: Advances in Neural Information Processing Systems. pp. 14004–14013 (2019)
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Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Transactions on Evolutionary Computation (2019)
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Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H., Zhao, B.Y.: Neural cleanse: Identifying and mitigating backdoor attacks in neural networks. In: IEEE Symposium on Security and Privacy (2019)
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