Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Zhu, Rui; Tang, Di; Tang, Siyuan; Wang, Zihao; Tao, Guanhong; Ma, Shiqing; Wang, XiaoFeng; Tang, Haixu

doi:10.14722/ndss.2024.24450

Proceedings 2024 Network and Distributed System Security Symposium 2024

DOI: 10.14722/ndss.2024.24450

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Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Rui Zhu,

Di Tang,

Siyuan Tang

et al.

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Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

Mengara,

Avila,

Falk

2024

IEEE Access

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Deep neural network (DNN) classifiers are potent instruments that can be used in various security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that impede or distort their learning process. For example, backdoor attacks involve polluting the DNN learning set with a few samples from one or more source classes, which are then labelled as a target class set by an attacker. Even if the DNN is trained on clean samples with no backdoors, this attack will still be successful if a backdoor pattern exists in the training data. Backdoor attacks are difficult to spot and can be used to make the DNN behave maliciously depending on the target selected by the attacker. In this study, we survey the literature and highlight the latest advances in backdoor attack strategies and defense mechanisms. We finalize the discussion on challenges and open issues, as well as future research opportunities.

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Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

Mengara,

Avila,

Falk

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

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Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Cited by 1 publication

References 42 publications

Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

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