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Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
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“Dedicated defense methods against both data poisoning and model poisoning attacks have been investigated, such as norm clipping [38], geometric median based Robust Federated Aggregation (RFA) [58] and robust learning rate [59].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [58]
In the citing paper
“Pillutla et al. [58] proposed a robust aggregation approach called RFA by replacing the weighted arithmetic mean with an approximate geometric median, so as to reduce the impact of the contaminated updates.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [58]
In the citing paper
“Almost all of the well-designed Byzantine-robust aggregators [22], [23], [58] still suffer from the curse of dimensionality.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [58]
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Li, Z., Sun, H., Xia, P., Xia, B., Rui, X., Zhang, W., Guo, Q., Fu, Z., & Li, B. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3472510 · Cited by 17
Invisible backdoor attack with attention and steganography
Chen, W., Xu, X., Wang, X., Zhou, H., Li, Z., & Chen, Y. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104208 · Cited by 12
Mitigating Cross-Modal Retrieval Violations With Privacy-Preserving Backdoor Learning
Liu, Q., Qiu, Y., Zhou, T., Xu, M., Qin, J., Ma, W., Zhang, F., & Cai, Z. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3489886 · Cited by 14
Contrastive Neuron Pruning for Backdoor Defense
Feng, Y., Ma, B., Liu, D., Zhang, Y., Cai, W., & Xia, Y. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3539466 · Cited by 12
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning
Wu, B., Chen, H., Zhang, M., Zhu, Z., Wei, S., Yuan, D., Zhu, M., Wang, R., Liu, L., & Shen, C. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02447-x · Cited by 16
Backdoor attacks against Hybrid Classical-Quantum Neural Networks
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Use Procedural Noise to Achieve Backdoor Attack
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Lotus: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
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Backdoor Defense via Test-Time Detecting and Repairing
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I2I Backdoor: Backdoor Attacks Against Image-to-Image Tasks
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Metadata
- Authors
- Liu, Y., Ma, X., Bailey, J., & Lu, F.
- Affiliations
- State Key Laboratory of Virtual Reality Technology and Systems (CN)
- Deakin University (AU)
- The University of Melbourne (AU)
- Peng Cheng Laboratory (CN)
- Corresponding author
- Lu, Feng
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 182-199
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3107337211
- Cited by
- 531
- References
- 59
- Citations by year
- 2026: 56 · 2025: 116 · 2024: 128 · 2023: 102 · 2022: 74 · 2021: 47 · 2020: 8
- FWCI
- 22.06
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61972012)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Keywords
- backdoor attacks, deep neural network, stealthy attacks, backdoor defense, adversarial learning, computer vision
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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