Robust Backdoor Attacks against Deep Neural Networks in Real Physical World

2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)(2021)

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摘要
Deep neural networks (DNN) have been widely deployed in various applications. However, many researches indicated that DNN is vulnerable to backdoor attacks. The attacker can create a hidden backdoor in target DNN model, and trigger the malicious behaviors by submitting specific backdoor instance. However, almost all the existing backdoor works focused on the digital domain, while few studies inves...
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Training,Deep learning,Privacy,Target recognition,Face recognition,Conferences,Neural networks
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