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Bag of tricks for backdoor learning

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Abstract

Deep learning models are vulnerable to backdoor attacks, where an adversary aims to fool the model via data poisoning, such that the victim models perform well on clean samples but behave wrongly on poisoned samples. While researchers have studied backdoor attacks in depth, they have focused on specific attack and defense methods, neglecting the impacts of basic training tricks on the effect of backdoor attacks. Analyzing these influencing factors helps facilitate secure deep learning systems and explore novel defense perspectives. To this end, we provide comprehensive evaluations using a weak clean-label backdoor attack on CIFAR10, focusing on the impacts of a wide range of neglected training tricks on backdoor attacks. Specifically, we concentrate on ten perspectives, e.g., batch size, data augmentation, warmup, and mixup, etc. The results demonstrate that backdoor attacks are sensitive to some training tricks, and optimizing the basic training tricks can significantly improve the effect of backdoor attacks. For example, appropriate warmup settings can enhance the effect of backdoor attacks by 22% and 6% for the two different trigger patterns, respectively. These facts further reveal the vulnerability of deep learning models to backdoor attacks.

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Data availability

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

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Funding

This work was supported by the National Natural Science Foundation of China for Joint Fund Project (No.U1936218), the National Natural Science Foundation of China (No.62302111)

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Authors and Affiliations

  1. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, 510006, China

    Ruitao Hou, Anli Yan, Hongyang Yan & Teng Huang

  2. Pazhou Lab, Guangzhou, 510330, China

    Ruitao Hou

  3. School of Cyberspace Security (School of Cryptology), Hainan University, Haikou, 570228, China

    Anli Yan

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  1. Ruitao Hou
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  2. Anli Yan
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  3. Hongyang Yan
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  4. Teng Huang
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Contributions

RH provided the main idea, designed the methodology and experiments, and prepared the manuscript. AY conducted the experimental analysis and manuscript preparation. HY and TH performed the data analysis, edited and reviewed the manuscript.

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Correspondence to Ruitao Hou.

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Hou, R., Yan, A., Yan, H. et al. Bag of tricks for backdoor learning. Wireless Netw (2024). https://doi.org/10.1007/s11276-024-03724-2

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