The DOI matches its Crossref record; 50 of the 59 references it cites were found in a registry or an index.
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Scope of validity of PSNR in image/video quality assessment
Huynh-Thu, Q., & Ghanbari, M. (2008) · Electronics Letters · doi:10.1049/el:20080522 · Cited by 2,959
Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00175 · Cited by 2,173
The PASCAL visual object classes challenge 2006 (VOC2006) results
Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079
Research works printed but not linked: 16
- Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012) · arXiv
- Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017) · arXiv
- Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
- Evtimov, I., et al.: Robust physical-world attacks on deep learning models. In: CVPR (2018)
- Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014) · arXiv
- Gu, T., Dolan-Gavitt, B., Garg, S.: Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733 (2017) · arXiv
- Guo, C., Rana, M., Cisse, M., Van Der Maaten, L.: Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 (2017) · arXiv
- Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. arXiv preprint arXiv:1907.07174 (2019) · arXiv
- Li, S., Zhao, B.Z.H., Yu, J., Xue, M., Kaafar, D., Zhu, H.: Invisible backdoor attacks against deep neural networks. arXiv preprint arXiv:1909.02742 (2019) · arXiv
- Li, Y., Zhai, T., Wu, B., Jiang, Y., Li, Z., Xia, S.: Rethinking the trigger of backdoor attack. arXiv preprint arXiv:2004.04692 (2020) · arXiv
- Liao, C., Zhong, H., Squicciarini, A., Zhu, S., Miller, D.: Backdoor embedding in convolutional neural network models via invisible perturbation. arXiv preprint arXiv:1808.10307 (2018) · arXiv
- Liu, Y., Li, Y., You, S., Lu, F.: Semantic guided single image reflection removal. arXiv preprint arXiv:1907.11912 (2019) · arXiv
- Sun, Z., Kairouz, P., Suresh, A.T., McMahan, H.B.: Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 (2019) · arXiv
- Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) · arXiv
- Yao, Y., Li, H., Zheng, H., Zhao, B.Y.: Latent backdoor attacks on deep neural networks. In: ACM CCS, pp. 2041–2055 (2019) · DOI
- Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017) · arXiv
Cited by
Sorted by citations
Items 301–400 of 536
Mo, M., Wang, C., & Bian, S. (2024) · Symmetry · doi:10.3390/sym16030299 · Cited by 4
SATBA: An Invisible Backdoor Attack Based on Spatial Attention
Zhou, H., Xu, X., Wang, X., & Bullock, L. B. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00133 · Cited by 4
Ottun, A.-R. O., Marasinghe, R., Elemosho, T., Liyanage, M., Ragab, M., Bagave, P., Westberg, M., Asadi, M., Boerger, M., Sandeepa, C., et al. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00092 · Cited by 4
Event Trojan: Asynchronous Event-Based Backdoor Attacks
Wang, R., Guo, Q., Li, H., & Wan, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72667-5_18 · Cited by 4
Data Leakage Attack via Backdoor Misclassification Triggers of Deep Learning Models
Yang, X., Luo, W., Zhang, L., Chen, Z., & Wang, J. (2022) · International Conference on Data Intelligence and Security (ICDIS) · doi:10.1109/icdis55630.2022.00017 · Cited by 5
Sikandar, H. S., Malik, S. U. R., Anjum, A., Khan, A., & Jeon, G. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.07.037 · Cited by 4
Lightweight machine unlearning in neural networkPreprint
Chen, K., Wang, Y., & Huang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05528 · Cited by 4
Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor AttacksPreprint
Li, Z., Zeng, Y., Xia, P., Liu, L., Fu, Z., & Li, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5365101 · Cited by 4
On the Credibility of Backdoor Attacks Against Object Detectors in the Physical World
Doan, B. G., Nguyen, D. Q., Lindquist, C., Montague, P., Abraham, T., Vel, O. D., Camtepe, S. A., Kanhere, S. S., Abbasnejad, E., & Ranasinghe, D. C. (2024) · Annual Computer Security Applications Conference · doi:10.1109/acsac63791.2024.00079 · Cited by 5
FEAT: Frequency Energy Based Backdoor Attack in Deep Neural Networks
Li, J., Chen, H., Gao, Y., Li, J., Wang, W., Yu, J., & Qiu, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127352 · Cited by 3
DMC-Watermark: A backdoor richer watermark for dual identity verification by dynamic mask covering
Zhu, Y., Wang, R., & Xia, D. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06608-w · Cited by 3
Yuan, C., Bai, J., Yuan, S., & Ni, W. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3607257 · Cited by 3
Wang, Y., Zhai, D., Xia, Y., & Liu, D. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3329688 · Cited by 4
Xiao, Y., Ma, J., Yi, P., & Chen, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650435 · Cited by 4
Wu, Y., Li, Q., Xiang, Y., Zheng, J., Wu, X., Han, Z., Liu, J., & Niu, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0821-8_29 · Cited by 3
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method
Jiang, Y., Tan, C. W., & Lam, K. (2024) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw61385.2024.10807033 · Cited by 7
BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution
Yang, X., Chen, T., Guo, L., Jiang, W., Guo, J. H., Li, Y., & He, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889523 · Cited by 4
Defense Against Syntactic Textual Backdoor Attacks With Token Substitution
He, X., Li, X., Li, Y., & Cheng, M. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3597216 · Cited by 3
Wu, S., Xiao, J., & Sang, J. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3620146 · Cited by 3
Textual Backdoor Defense via Poisoned Sample Recognition
Shao, K., Zhang, Y., Yang, J., & Liu, H. (2021) · Applied Sciences · doi:10.3390/app11219938 · Cited by 3
Physical Backdoor Attack using Multi-Trigger to Same Class
Singh, G. P., Kumar, J., & Mohapatra, R. K. (2023) · International Conference on Artificial Intelligence and Signal Processing (AISP) · doi:10.1109/aisp57993.2023.10134794 · Cited by 3
Ensuring User Privacy and Model Security via Machine Unlearning: A Review
Tang, Y., Cai, Z., Liu, Q., Zhou, T., & Ni, Q. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.032307 · Cited by 3
Enhancing IoT Security: Federated Learning with GANs for Effective Attack Detection
Saeed, B., Arshad, S., Saeed, S. M. U., & Azam, M. A. (2023) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast59916.2023.10713010 · Cited by 3
Towards Physical World Backdoor Attacks Against Skeleton Action Recognition
Zheng, Q., Yu, Y., Yang, S., Liu, J., Lam, K., & Kot, A. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73195-2_13 · Cited by 3
ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
Alam, M., Lamri, H., & Maniatakos, M. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11133199 · Cited by 5
DLP: towards active defense against backdoor attacks with decoupled learning process
Ying, Z., & Wu, B. L. (2023) · Cybersecurity · doi:10.1186/s42400-023-00141-4 · Cited by 10
Unlabeled backdoor poisoning on trained-from-scratch semi-supervised learning
Feng, L., Qian, Z., Zhang, X., & Li, S. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119453 · Cited by 3
Mitigating Backdoor Attacks on Deep Neural Networks
Fu, H., Sarmadi, A., Krishnamurthy, P., Garg, S., & Khorrami, F. (2023) · Springer eBooks · doi:10.1007/978-3-031-40677-5_16 · Cited by 3
Hidden in Plain Sight: Adversarial Attack on Wavelet-Based Banknote Authentication
Knaup, J., Holst, C., & Lohweg, V. (2024) · IEEE International Forum on Research and Technology for Society and Industry (RTSI) · doi:10.1109/rtsi61910.2024.10761182 · Cited by 3
Backdoor Pony: Evaluating backdoor attacks and defenses in different domains
Mercier, A., Smolin, N., Sihlovec, O., Koffas, S., & Picek, S. (2023) · SoftwareX · doi:10.1016/j.softx.2023.101387 · Cited by 3
Harnessing dynamic heterogeneous redundancy to empower deep learning safety and security
Zhang, F., Chen, X., Huang, W., Wu, J., Zhang, Z., Zhou, C., Li, J., Peng, Z., Wei, G., Yang, G., et al. (2024) · Security and Safety · doi:10.1051/sands/2024011 · Cited by 2
Sample-independent federated learning backdoor attack in speaker recognition
Xu, W., Xu, Y., & Zhang, S. (2025) · Cluster Computing · doi:10.1007/s10586-024-04837-1 · Cited by 2
Exploring Inherent Backdoors in Deep Learning Models
Tao, G., Cheng, S., Wang, Z., Ma, S., An, S., Liu, Y., Shen, G., Zhang, Z., Mao, Y., & Zhang, X. (2024) · Annual Computer Security Applications Conference · doi:10.1109/acsac63791.2024.00078 · Cited by 3
Self-citationRed alarm: Controllable backdoor attack in continual learning
Gao, R., & Liu, W. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107479 · Cited by 2
BadSTR: Backdoor Attack on Scene Text Recognition in IoT
Wang, Q., Hu, Y., Jia, X., Wu, G., Ren, Y., Pan, G., & Cheng, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3586440 · Cited by 2
The Image Scaling Attack: Unveiling the Risks in Traffic Sign Classification
Reif, A. K., Stolz, T., Picek, S., Ramírez-Agudelo, O. H., & Karl, M. (2025) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw67616.2025.00041 · Cited by 2
LBP-LSB Co-Optimisation for Dynamic Unseen Backdoor Attacks
Luo, Z., Li, F., & Peng, J. (2025) · Electronics · doi:10.3390/electronics14214216 · Cited by 2
When Deepfake Meets Backdoor: Leveraging GAN Fingerprints for Data Poisoning Attack
Zhao, Y. C., Ma, Y., Ge, Y., Zhao, L., Zhao, L., & Wang, Q. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3633381 · Cited by 2
Cracking the Code of Backdoor Attacks With Confidence Consistency
Zhuang, Z., Ding, Y., Zheng, S., Shao, J., Zhu, D., & Wang, H. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3653553 · Cited by 2
Yuan, S., Xu, G., Li, H., Zhang, R., Qian, X., Cao, H., & Zhao, Q. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3665300 · Cited by 3
Jo, S., & Cho, Y. (2026) · Cybersecurity · doi:10.1186/s42400-025-00477-z · Cited by 3
BadSAD: Clean-Label Backdoor Attacks Against Deep Semi-Supervised Anomaly Detection
Cheng, H., Xu, D., & Yuan, S. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402072 · Cited by 2
An Invisible Backdoor Attack based on DCT-Injection
Xiao, T., Deng, X., & Jiang, W. (2022) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus55513.2022.9987040 · Cited by 2
Backdoor Attack on Deep Neural Networks in Perception Domain
Mo, X., Zhang, L. Y., Sun, N., Luo, W., & Gao, S. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191661 · Cited by 2
Rethinking Data Bias: Dataset Copyright Protection via Embedding Class-Wise Hidden Bias
Jang, J., Han, B., Kim, J., & Youn, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72664-4_1 · Cited by 2
Clean-Label Attack on Face Authentication Systems Through Rolling Shutter Mechanism
Wang, Y., Li, H., Zhang, L., Hu, Y., & Kot, A. C. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3493804 · Cited by 3
Backdoor for Debias: Mitigating Model Bias With Backdoor Attack-Based Artificial Bias
Wu, S., He, Q., Yu, J., & Sang, J. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3548657 · Cited by 2
Semantic-Aware Framework for Backdoor Detection in AI Models
Fu, K., & Dai, J. (2025) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.378675 · Cited by 2
DarkHash: A Data-Free Backdoor Attack Against Deep Hashing
Zhou, Z., Deng, M., Song, Y., Zhang, H., Wan, W., Hu, S., Li, M., Zhang, L. Y., & Yao, D. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3593813 · Cited by 3
Esipov, D. A., Buchaev, A. Y., Kerimbay, A., Puzikova, Y. V., Saidumarov, S. K., Sulimenko, N. S., Popov, I. Y., & Karmanovskiy, N. S. (2023) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2023-23-4-720-733 · Cited by 5
ScanFed: Scalable Behavior-Based Backdoor Detection in Federated Learning
Ning, R., Li, J., Xin, C., Wang, C., Xu, L., Gazda, R., Cho, J.-H., & Wu, H. (2023) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00011 · Cited by 2
The Victim and The Beneficiary: Exploiting a Poisoned Model to Train a Clean Model on Poisoned Data
Zhu, Z., Wang, R., Zou, C., & Jing, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00021 · Cited by 3
WaTrojan: Wavelet domain trigger injection for backdoor attacks
Zhang, Z., Ding, J., Zhang, Q., & Deng, Q. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103767 · Cited by 2
Unlearnable Examples for Time Series
Jiang, Y., Ma, X., Erfani, S., & Bailey, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2266-2_17 · Cited by 2
Self-citationData Poisoning Quantization Backdoor Attack
Huynh, T., Tran, A., Doan, K. D., & Pham, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72907-2_3 · Cited by 3
What Makes Vision Transformers Robust Towards Bit-Flip Attack?
Zhou, X., Kundu, S., Chen, D., Huang, J., & Beerel, P. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78186-5_28 · Cited by 2
AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration
Song, Z., Gao, W., Zhang, Z., & Lu, J. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00136 · Cited by 2
Acoustic backdoor attacks on speech recognition via frequency offset perturbation
Tang, Y., Xu, X., & Sun, L. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113188 · Cited by 2
Arms Race in Deep Learning: A Survey of Backdoor Defenses and Adaptive Attacks
Mo, X., Sun, N., Zhang, L. Y., Luo, W., Gao, S., & Xiang, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8183-9_24 · Cited by 2
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing
Guo, D., Hu, M., Guan, Z., Guo, J., Hartvigsen, T., & Li, S. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761408 · Cited by 5
Zhang, P., Liu, G., Chen, Z., Guo, J., & Liu, P. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9824166 · Cited by 2
Debiasing backdoor attack: A benign application of backdoor attack in eliminating data bias
Wu, S., He, Q., Zhang, Y., Lü, D., & Sang, J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119171 · Cited by 3
Invisible backdoor learning in regional transform domain
Sun, Y., Lu, Y., Yan, X., & Wang, X. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09506-3 · Cited by 2
CatchBackdoor: Backdoor Detection via Critical Trojan Neural Path Fuzzing
Jin, H., Chen, R., Chen, J., Zheng, H., Zhang, Y., & Wang, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72970-6_6 · Cited by 2
Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection
Zhou, X., Jiang, W., Qi, S., & Mu, Y. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/477 · Cited by 2
Trojan Attacks and Defense for Speech Recognition
Zong, W., Chow, Y.-W., Susilo, W., & Kim, J. (2022) · Communications in computer and information science · doi:10.1007/978-981-16-9576-6_14 · Cited by 2
Inconspicuous Data Augmentation Based Backdoor Attack on Deep Neural Networks
Xu, C., Liu, W., Zheng, Y., Wang, S., & Chang, C.-H. (2022) · IEEE International System-on-Chip Conference (SOCC) · doi:10.1109/socc56010.2022.9908113 · Cited by 2
Ye, J., Mao, Y., Song, J., Wang, X., Jin, C., & Song, M. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20219 · Cited by 1
DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor Attack
Ma, B., Wang, J., Wang, D., & Meng, B. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07328-5 · Cited by 1
A Model Interpretability-Based Defense Against Clean-Label Backdoor Attacks
Shuhan, Z., Jiang, W., Lu, Y., & Li, H. (2024) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip64812.2024.10873733 · Cited by 1
MACS-BNet: A Stealthy Multiconstraint Adversarial Backdoor Network Against Compressed Learning
Zhu, H., Wu, W. D., Peng, H., & Zhao, D. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3563088 · Cited by 1
Stealthy Physical Backdoor Attacks Against Traffic Sign Recognition Systems
Jiang, W., Li, H., Lu, Y., Yuan, S., Zhang, R., Wang, Z., Song, Q., & Liu, D. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161037 · Cited by 1
SPD: Shallow Backdoor Protecting Deep Backdoor Against Backdoor Detection
Yuan, S., Li, X., Cao, X., Zhang, H. L., Zhu, M., & DENG, R. H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00384 · Cited by 1
SWBA: A Sparse and Stealthy Backdoor Attack via Discrete Wavelet Transform for Image Classification
Liu, Y., Ru, X., Li, X., & Li, W. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3701242 · Cited by 1
Self-citationBackdoor Attacks and Generative Model Fairness: Current Trends and Future Research Directions
Holland, R., Pal, S., Pan, L., & Zhang, L. Y. (2024) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets59351.2024.10427172 · Cited by 1
Unlearnable Examples Detection via Iterative Filtering
Yu, Y., Zheng, Q., Yang, S., Yang, W., Liu, J., Lu, S., Tan, Y., Lam, K., & Kot, A. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72359-9_18 · Cited by 2
Invisible Backdoor Attack Through Singular Value Decomposition
Chen, W., Xu, X., Wang, X., Li, Z., & Chen, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8490-5_13 · Cited by 2
Enhancing robustness of backdoor attacks against backdoor defenses
Hu, B., Guo, K., Ren, S., & Fang, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126355 · Cited by 1
Covert and Potent: A Weather-Camouflaged Backdoor Attacks on Self-Supervised Learning
Yang, W., Yang, Y., Liu, B., & Xiao, B. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888213 · Cited by 1
Effective Backdoor Learning on Open-Set Face Recognition Systems
Voth, D., Dane, L., Grebe, J. H., Peitz, S., & Terhörst, P. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00109 · Cited by 2
EUN: Enhanced unlearnable examples generation approach for privacy protection
Chen, X., Xu, Y., Zhang, S., Yan, J., Xu, W., & He, X. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104388 · Cited by 1
Clean-label backdoor attack via sample-customized feature alignment
Zhang, C., Sun, S., Tu, J., Chen, X., & Wang, D. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129481 · Cited by 1
PoisonPatch: Natural Adversarial Patches via Diffusion Models and Federated Learning Poisoning
Wang, Y., Fu, Y., Kong, W., Liu, C., & Su, S. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3620155 · Cited by 1
HBIpFL: hypernetwork and backdoor isolation personalized federated learning
Chen, J., Jiang, H., Xi, Y., Li, C., Xu, E., Xu, S., & Wang, J. (2025) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-025-00207-x · Cited by 1
Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models
Wang, Z., Zhang, J., Shan, S., & Chen, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3644016 · Cited by 8
Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networks
Li, J., Wu, B. Y., Xia, X., Liu, X., Yi, X., & Zhang, X. (2025) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1109/raid67961.2025.00025 · Cited by 4
QuEST: Quantization-Conditioned Efficient Stealthy Trojan
Lu, L., Pang, S., Wang, J., Gu, X., Liu, Y., Liu, X., & Zhou, Y. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3671079 · Cited by 3
Self-citationPrototype Guided Backdoor Defense via Activation Space Manipulation
Amula, V. A., Samavedam, S., Saini, S., Gupta, A., & Narayanan, P. J. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00212 · Cited by 2
Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
Herath, K., Zhao, J. Y., & Bagchi, S. (2026) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn69566.2026.00032 · Cited by 1
WARP: A Word-Level Backdoor Attack Targeting RAG Systems via Retrieval Corpus Poisoning
Liu, H., Zhou, Y., Dong, L., Li, W., & Yu, S. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770854.3780227 · Cited by 1
Hu, X., Lan, Y., Gao, R., Meng, G., & Chen, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95391-1_47 · Cited by 1
I Know Your Triggers: Defending Against Textual Backdoor Attacks with Benign Backdoor Augmentation
Gao, Y., Stokes, J. W., Prasad, M., Marshall, A. T., Fawaz, K., & Kıcıman, E. (2022) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom55135.2022.10017466 · Cited by 2
Computational Color Constancy-Based Backdoor Attacks
Vršnak, D., Sabolić, I., Subašić, M., & Lončarić, S. (2023) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa58351.2023.10278694 · Cited by 1
Engravings, Secrets, and Interpretability of Neural Networks
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
Gao, Y., Chen, H., Li, J., Wang, X., Alharbi, A., Tolba, A. K. R., & Xia, F. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3399475 · Cited by 1
IBAQ: Frequency-Domain Backdoor Attack Threatening Autonomous Driving via Quadratic Phase
Qiu, J., Chen, H., Li, J., Gao, Y., Li, J., & Wang, X. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3673904 · 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-citationBABE : 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-citationBackdoor Attacks on Bimodal Salient Object Detection with RGB-Thermal Data
Yin, W., Zhu, B., Xie, Y., Zhou, P., & Feng, D. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681096 · Cited by 1
BDEL: A Backdoor Attack Defense Method Based on Ensemble Learning
Xing, Z., Lan, Y., Yu, Y., Cao, Y., Yang, X., Yu, Y., & Yu, D. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0116-5_18 · Cited by 1
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 ›
Are you an author, or do you see a problem with this page? Request a review