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Trojaning Attack on Neural Networks
Open accessAbstract
With the fast spread of machine learning techniques, sharing and adopting public machine learning models become very popular.This gives attackers many new opportunities.In this paper, we propose a trojaning attack on neural networks.As the models are not intuitive for human to understand, the attack features stealthiness.Deploying trojaned models can cause various severe consequences including endangering human lives (in applications like autonomous driving).We first inverse the neural network to generate a general trojan trigger, and then retrain the model with reversed engineered training data to inject malicious behaviors to the model.The malicious behaviors are only activated by inputs stamped with the trojan trigger.In our attack, we do not need to tamper with the original training process, which usually takes weeks to months.Instead, it takes minutes to hours to apply our attack.Also, we do not require the datasets that are used to train the model.In practice, the datasets are usually not shared due to privacy or copyright concerns.We use five different applications to demonstrate the power of our attack, and perform a deep analysis on the possible factors that affect the attack.The results show that our attack is highly effective and efficient.The trojaned behaviors can be successfully triggered (with nearly 100% possibility) without affecting its test accuracy for normal input and even with better accuracy on public dataset.Also, it only takes a small amount of time to attack a complex neuron network model.In the end, we also discuss possible defense against such attacks.
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“An alternative to this assumption would be incorporating an additional party or a public bulletin board (see, e.g., [398]) into the model that is known to the clients and trusted to not collude with the server.”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [398]
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“By sharing the model parameters instead of data, FL introduces new attack surfaces at training time by enhancing the capabilities of the adversary [16].”
From Vulnerabilities in federated learning (2021) · cites this paper as [16]
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Hobbs, N., Papakonstantinou, P. A., & Vaidya, J. S. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2024.3358759 · Cited by 1
Bag of tricks for backdoor learning
Hou, R., Yan, A., Yan, H., & Huang, T. (2024) · Wireless Networks · doi:10.1007/s11276-024-03724-2 · Cited by 1
AdvBinSD: Poisoning the Binary Code Similarity Detector via Isolated Instruction Sequences
Yi, X., Li, G., Ding, A., Zheng, Y., Li, Y., & Li, J. (2023) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00183 · Cited by 1
Li, J., Chen, H., Ni, Z., Gao, Y., Liu, W., & Jiang, N. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3413720 · Cited by 1
B³A: Bokeh Based Backdoor Attack with Feature Restrictions
Li, J., Chen, H., Gao, Y., Lin, K., & Liu, Y. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00099 · Cited by 1
Self-citationDifferent Attack and Defense Types for AI Cybersecurity
Zou, J., Zhang, S., & Qiu, M. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5498-4_14 · Cited by 1
DPC: Filtering Out Patch-Based Poisoned Samples with Differential Privacy
Yan, Y., Tang, P., Chen, R., Han, Q., & Du, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70890-9_15 · Cited by 1
AutoML in the Face of Adversity: Securing Mobility Predictions in NWDAF
Atiiq, S. A., Gehrmann, C., Yuan, Y., & Sternby, J. (2024) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec62297.2024.10710314 · Cited by 1
BABE : Backdoor attack with bokeh effects via latent separation suppression
Li, J., Chen, H., Gao, Y., Guo, S., Lin, K., Liu, Y., & Sun, P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109462 · Cited by 1
Self-citationTrojFair: Trojan Fairness Attacks
Xue, J., Zheng, M., Sheng, Y., Yang, L., Lou, Q., & Jiang, L. (2023) · doi:10.1145/3689217.3690620 · Cited by 3
WBP: Training-Time Backdoor Attacks Through Hardware-Based Weight Bit Poisoning
Cai, K., Zhang, Z., Lou, Q., & Yao, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73650-6_11 · Cited by 1
Patch Based Backdoor Attack on Deep Neural Networks
Manna, D., & Tripathy, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-80020-7_24 · Cited by 1
DevilDiffusion: Embedding Hidden Noise Backdoors into Diffusion Models
Aiken, W., Branco, P., & Jourdan, G.-V. (2024) · Conference on Privacy, Security and Trust · doi:10.1109/pst62714.2024.10788056 · Cited by 1
To Share or Hide: Confidential Model Compilation as a Service with Privacy-Preserving Transparency
Qin, K., & Gu, D. (2024) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds64841.2024.00022 · Cited by 1
Clean Label Attacks Against SLU Systems
Xinyuan, H. L., Joshi, S., Thebaud, T., Villalba, J., Dehak, N., & Khudanpur, S. P. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832144 · Cited by 3
Safely Knowledge Transfer from Source Models via an Iterative Pruning Based Learning Approach
Lu, X. S., Zhang, J., Yao, S., & Huang, B. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831797 · Cited by 1
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Chen, X., Liu, C., Li, B., Lu, K., & Song, D. (2017) · arXiv · doi:10.48550/arxiv.1712.05526 · Cited by 1,039
Analyzing Federated Learning through an Adversarial Lens
Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. (2018) · arXiv · doi:10.48550/arxiv.1811.12470 · Cited by 703
Metadata
- Authors
- Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., & Zhang, X.
- Affiliations
- Purdue University West Lafayette (US)
- Nanjing University (CN)
- Venue
- Proceedings 2018 Network and Distributed System Security Symposium
- Publisher
- Internet Society
- Published
- 2018
- OpenAlex
- W2753783305
- Cited by
- 1,270
- References
- 49
- Citations by year
- 2026: 114 · 2025: 193 · 2024: 206 · 2023: 184 · 2022: 149 · 2021: 185 · 2020: 153 · 2019: 59 · 2018: 26
- FWCI
- 43.17
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Network and Distributed System Security Symposium (publishedVersion, public-domain)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- Purdue e-Pubs (Purdue University System) (submittedVersion)
- Funders
- National Science Foundation (1748764, 1320444, 1409668)
- Defense Advanced Research Projects Agency (FA-8650-15-C-7562, FA8650-15C)
- Office of Naval Research (N000141712947, N000141410468)
- Sandia National Laboratories (1701331)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Physical Unclonable Functions (PUFs) and Hardware Security
- Keywords
- neural network security, stealthy attacks, model poisoning, autonomous driving safety
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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