Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

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

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143

Overcoming catastrophic forgetting in neural networks

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899

Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436

Calibrating Noise to Sensitivity in Private Data Analysis

Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272

Robust estimation of a location parameter

Huber, P. J. (1964) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177703732 · Cited by 7,228

Evaluating Large Language Models Trained on Code

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436

Backdoor Learning: A Survey

Li, Y., Jiang, Y., Li, Z., & Xia, S.-T. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3182979 · Cited by 682

Unleashing the potential of prompt engineering for large language models

CHEN, B. ‐., Zhang, Z., Langrené, N., & Zhu, S. (2025) · Patterns · doi:10.1016/j.patter.2025.101260 · Cited by 1,097

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

Shejwalkar, V., Houmansadr, A., Kairouz, P., & Ramage, D. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833647 · Cited by 292

Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity

Zhou, S., Liu, C., Ye, D., Zhu, T., Zhou, W., & Yu, P. S. (2022) · ACM Computing Surveys · doi:10.1145/3547330 · Cited by 195

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

Cinà, A. E., Grosse, K., Demontis, A., Vascon, S., Zellinger, W., Moser, B. A., Oprea, A., Biggio, B., Pelillo, M., & Roli, F. (2023) · ACM Computing Surveys · doi:10.1145/3585385 · Cited by 152

Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey

Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152

Adversarial XAI Methods in Cybersecurity

Kuppa, A., & Le‐Khac, N. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3117075 · Cited by 151

Security in internet of things: a review on approaches based on blockchain, machine learning, cryptography, and quantum computing

Cherbal, S., Zier, A., Hebal, S., Louail, L., & Annane, B. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-023-05616-2 · Cited by 152

Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review

Brauneck, A., Schmalhorst, L., Kazemi Majdabadi, M. M., Bakhtiari, M., Völker, U., Baumbach, J., Baumbach, L., & Buchholtz, G. (2023) · Journal of Medical Internet Research · doi:10.2196/41588 · Cited by 130

Next Generation Advanced Transceiver Technologies for 6G and Beyond

You, C., Cai, Y., Liu, Y., Renzo, M. D., Duman, T. M., Yener, A., & Swindlehurst, A. L. (2025) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2025.3536557 · Cited by 156

Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

Nguyen, T. D., Nguyen, T., Nguyen, P. L., Pham, H. H., Doan, K. D., & Wong, K.-S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107166 · Cited by 130

Adversarial Neuron Pruning Purifies Backdoored Deep ModelsPreprint

Wu, D., & Wang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14430 · Cited by 125

Diffusion Models for Imperceptible and Transferable Adversarial Attack

Chen, J., Chen, H., Chen, K., Zhang, Y., Zou, Z., & Shi, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3480519 · Cited by 154

Threats to Training: A Survey of Poisoning Attacks and Defenses on Machine Learning Systems

Wang, Z., Ma, J., Wang, X., Hu, J., Qin, Z., & Ren, K. (2022) · ACM Computing Surveys · doi:10.1145/3538707 · Cited by 118

A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

Neto, H. N. C., Hribar, J., Dusparić, I., Mattos, D. M. F., & Fernandes, N. C. (2023) · IEEE Access · doi:10.1109/access.2023.3269980 · Cited by 114

Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff

Borgnia, E., Cherepanova, V., Fowl, L., Ghiasi, A., Geiping, J., Goldblum, M., Goldstein, T., & Gupta, A. K. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414862 · Cited by 100

Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models

Shan, S., Ding, W., Passananti, J., Wu, S. L., Zheng, H.-T., & Zhao, B. Y. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00207 · Cited by 166

Boundary Unlearning: Rapid Forgetting of Deep Networks via Shifting the Decision Boundary

Chen, M., Gao, W., Liu, G., Peng, K., & Wang, C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00750 · Cited by 95

Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

Yaacoub, J.-P. A., Noura, H., & Salman, O. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.001 · Cited by 106

An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Guo, W., Tondi, B., & Barni, M. (2022) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2022.3190213 · Cited by 91

An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

Jiang, W., Synovic, N., Hyatt, M., Schorlemmer, T. R., Sethi, R., Lu, Y.-H., Thiruvathukal, G. K., & Davis, J. C. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00206 · Cited by 76

OQFL: An Optimized Quantum-Based Federated Learning Framework for Defending Against Adversarial Attacks in Intelligent Transportation Systems

Yamany, W., Moustafa, N., & Turnbull, B. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3130906 · Cited by 87

A Comprehensive Study on the Robustness of Deep Learning-Based Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking

Mei, S., Lian, J., Wang, X., Su, Y., Ma, M., & Chau, L. (2024) · Journal of Remote Sensing · doi:10.34133/remotesensing.0219 · Cited by 85

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Neupane, S., Mitra, S., Fernandez, I. A., Saha, S., Mittal, S., Chen, J., Pillai, N., & Rahimi, S. (2024) · IEEE Access · doi:10.1109/access.2024.3363657 · Cited by 96

Property Inference from Poisoning

Mahloujifar, S., Ghosh, E., & Chase, M. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833623 · Cited by 67

A Survey on Data Poisoning Attacks and Defenses

Fan, J., Yan, Q., Li, M., Qu, G., & Yang, X. (2022) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc55868.2022.00014 · Cited by 75

Federated Learning for Enhanced Cybersecurity and Trustworthiness in 5G and 6G Networks: A Comprehensive Survey

Blika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 82

Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy

Shaik, T., Tao, X., Xie, H., Li, L., Zhu, X., & Li, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3486109 · Cited by 113

Industry-Scale Orchestrated Federated Learning for Drug Discovery

Oldenhof, M., Ács, G., Pejó, B., Schuffenhauer, A., Holway, N., Sturm, N., Dieckmann, A., Fortmeier, O., Boniface, E., Mayer, C., et al. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26847 · Cited by 67

BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation ModelsPreprint

Chen, K., Meng, Y., Sun, X., Guo, S., Zhang, T., Li, J., & Fan, C. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02467 · Cited by 84

Brain-Inspired Learning, Perception, and Cognition: A Comprehensive Review

Jiao, L., Ma, M., He, P., Geng, X., Liu, X., Liu, F., Ma, W., Yang, S., Hou, B., & Tang, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3401711 · Cited by 54

Data poisoning attacks in intelligent transportation systems: A survey

Wang, F., Wang, X., & Ban, X. J. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104750 · Cited by 46

Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks

Cotroneo, D., Improta, C., Liguori, P., & Natella, R. (2024) · Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension · doi:10.1145/3643916.3644416 · Cited by 45

Trends in Smart Grid Cyber-Physical Security: Components, Threats, and Solutions

Manias, D. M., Saber, A. M., Radaideh, M. I., Gaber, A. T., Maniatakos, M., Zeineldin, H. H., Svetinović, D., & El-Saadany, E. F. (2024) · IEEE Access · doi:10.1109/access.2024.3477714 · Cited by 51

Backdoors in Neural Models of Source Code

Ramakrishnan, G., & Albarghouthi, A. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956690 · Cited by 61

Security for Machine Learning-based Software Systems: A Survey of Threats, Practices, and Challenges

Chen, H., & Babar, M. A. (2023) · ACM Computing Surveys · doi:10.1145/3638531 · Cited by 66

Toward Stealthy Backdoor Attacks Against Speech Recognition via Elements of Sound

Cai, H., Zhang, P., Dong, H., Xiao, Y., Koffas, S., & Li, Y. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3404885 · Cited by 47

Untargeted Backdoor Attack Against Object Detection

Luo, C., Li, Y., Jiang, Y., & Xia, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095980 · Cited by 39

Machine learning security and privacy: a review of threats and countermeasures

Paracha, A., Arshad, J., Farah, M. A. B., & Ismail, K. N. (2024) · EURASIP Journal on Information Security · doi:10.1186/s13635-024-00158-3 · Cited by 47

Too good to be safe: Tricking lane detection in autonomous driving with crafted perturbations

Jing, P., Tang, Q., Du, Y., Xue, L., Luo, X., Wang, T., Nie, S., & Wu, S. (2021) · USENIX Security Symposium · Cited by 34

Backdooring Multimodal Learning

Han, X., Wu, Y., Zhang, Q., Zhou, Y. H., Xu, Y., Qiu, H., Xu, G., & Zhang, T. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00031 · Cited by 33

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

Qin, Z., Yao, L., Chen, D., Li, Y., Ding, B., & Cheng, M. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599898 · Cited by 39

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

Mengara, O., Avila, A. R., & Falk, T. H. (2024) · IEEE Access · doi:10.1109/access.2024.3355816 · Cited by 35

On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective

Wang, M., Yang, N., Gunasinghe, D. H., & Weng, N. (2023) · Computers · doi:10.3390/computers12100209 · Cited by 29

A novel dimensional variational prototypical network for industrial few-shot fault diagnosis with unseen faults

Peng, C., Chen, L., Hao, K., Chen, S., Cai, X., & Wei, B. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104133 · Cited by 29

Backdoor Attacks Against Dataset Distillation

Liu, Y., Li, Z., Michael Backes, A., Shen, Y., & Zhang, Y. (2023) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2023.24287 · Cited by 39

Resource Management, Security, and Privacy Issues in Semantic Communications: A Survey

Won, D., Woraphonbenjakul, G., Wondmagegn, A. B., Tran, A.-T., Lee, D., Lakew, D. S., & Cho, S. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3471685 · Cited by 35

Uncovering Covert Attacks on EV Charging Infrastructure: How OCPP Backend Vulnerabilities Could Compromise Your System

Sarieddine, K., Sayed, M. A., Torabi, S., Atallah, R. F., Jafarigiv, D., Assi, C. M., & Debbabi, M. (2024) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3634737.3644999 · Cited by 27

Big data analytics and artificial intelligence aspects for privacy and security concerns for demand response modelling in smart grid: A futuristic approach

Reka, S. S., Dragičević, T., Venugopal, P., Ravi, V. K. K., & Rajagopal, M. K. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35683 · Cited by 27

Artificial Intelligence in Renewable Energy Systems: Applications and Security Challenges

Xiang, H. Y., Li, X., Liao, X., Cui, W., Liu, F., & Li, D. (2025) · Energies · doi:10.3390/en18081931 · Cited by 27

A Survey on Trusted Distributed Artificial Intelligence

Ağca, M. A., Faye, S., & Khadraoui, D. (2022) · IEEE Access · doi:10.1109/access.2022.3176385 · Cited by 30

Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial TrainingPreprint

Tao, L., Feng, L., Yi, J., Huang, S.-J., & Chen, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04716 · Cited by 31

A Survey on Backdoor Attack and Defense in Natural Language Processing

Sheng, X., Han, Z., Li, P., & Chang, X. (2022) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs57517.2022.00086 · Cited by 30

An adaptive robust defending algorithm against backdoor attacks in federated learning

Wang, Y., Zhai, D.-H., He, Y., & Xia, Y. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.01.026 · Cited by 25

Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models

Huang, Y., Juefei-Xu, F., Guo, Q., Zhang, J., Wu, Y., Hu, M., Li, T., Pu, G., & Liu, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30110 · Cited by 49

Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings

Moayeri, M., & Feizi, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00758 · Cited by 24

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

Roux, Q. L., Bourbao, E., Teglia, Y., & Kallas, K. (2024) · IEEE Access · doi:10.1109/access.2024.3382584 · Cited by 30

Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System

Shahid, A. R., Imteaj, A., Wu, P. Y., Igoche, D., & Alam, T. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022015 · Cited by 22

Free Fine-tuning: A Plug-and-Play Watermarking Scheme for Deep Neural Networks

Wang, R., Ren, J., Li, B., She, T., Zhang, W., Fang, L., Chen, J., & Wang, L. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612331 · Cited by 21

Adversarial Reinforcement Learning Based Data Poisoning Attacks Defense for Task-Oriented Multi-User Semantic Communication

Peng, J., Xing, H., Xu, L., Luo, S., Dai, P., Feng, L., Song, J. H., Zhao, B., & Xiao, Z. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3447087 · Cited by 20

Blockchain for Security in Digital Twins

Suleiman, R., Sai, A. M. V. V., Yu, W., & Wang, C. (2025) · Future Internet · doi:10.3390/fi17090385 · Cited by 24

Turning Federated Learning Systems Into Covert Channels

Costa, G., Pinelli, F., Soderi, S., & Tolomei, G. (2022) · IEEE Access · doi:10.1109/access.2022.3229124 · Cited by 35

On the Effectiveness of Adversarial Training Against Backdoor Attacks

Gao, Y., Wu, D., Zhang, J., Gan, G., Xia, S., Niu, G., & Sugiyama, M. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3281872 · Cited by 18

Data Encryption and Anonymization Techniques for Enhanced Information System Security and Privacy

Pratomo, A. B., Mokodenseho, S., & Aziz, A. M. (2023) · West Science Information System and Technology · doi:10.58812/wsist.v1i01.176 · Cited by 18

Holistic Adversarial Robustness of Deep Learning Models

Chen, P., & Liu, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26797 · Cited by 23

It Is All about Data: A Survey on the Effects of Data on Adversarial Robustness

Xiong, P., Tegegn, M., Sarin, J. S., Pal, S., & Rubin, J. (2023) · ACM Computing Surveys · doi:10.1145/3627817 · Cited by 19

Cybersecurity and privacy in smart bioprinting

Isichei, J., Khorsandroo, S., & Desai, S. S. (2023) · Bioprinting · doi:10.1016/j.bprint.2023.e00321 · Cited by 17

The revolution and vision of explainable AI for Android malware detection and protection

Ullah, S., Li, J., Ullah, F., Chen, J., Ali, I., Khan, S., Ahad, A., & Leung, V. C. M. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101320 · Cited by 19

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset ReleasePreprint

Fowl, L., Chiang, P.-Y., Goldblum, M., Geiping, J., Bansal, A., Czaja, W., & Goldstein, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.02683 · Cited by 48

Roadmap of Adversarial Machine Learning in Internet of Things-Enabled Security Systems

Harbi, Y., Medani, K., Gherbi, C., Aliouat, Z., & Harous, S. (2024) · Sensors · doi:10.3390/s24165150 · Cited by 16

Reliable Model Watermarking: Defending against Theft without Compromising on Evasion

Zhu, H., Liang, S., Hu, W., Li, F., Jia, J., & Wang, S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681610 · Cited by 20

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data AugmentationsPreprint

Borgnia, E., Geiping, J., Cherepanova, V., Fowl, L., Gupta, A. K., Ghiasi, A., Huang, F., Goldblum, M., & Goldstein, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.02079 · Cited by 43

A decentralized data evaluation framework in federated learning

Bhatia, L., & Samet, S. (2023) · Blockchain Research and Applications · doi:10.1016/j.bcra.2023.100152 · Cited by 15

Stealthy Backdoor Attack Against Speaker Recognition Using Phase-Injection Hidden Trigger

Ye, Z., Yan, D., Dong, L., Deng, J., & Yu, S. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3293429 · Cited by 15

Assessing Wearable Human Activity Recognition Systems Against Data Poisoning Attacks in Differentially-Private Federated Learning

Shahid, A. R., Imteaj, A., Badsha, S., & Hossain, M. Z. (2023) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp58114.2023.00085 · Cited by 14

Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks

Butt, U., Hussien, O. A. A. M., Hasanaj, K., Shaalan, K. F., Hassan, B., & Al‐Khateeb, H. (2023) · Algorithms · doi:10.3390/a16120549 · Cited by 17

Defending against Poisoning Backdoor Attacks on Federated Meta-learning

Chen, C.-L., Babakniya, S., Paolieri, M., & Golubchik, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3523062 · Cited by 14

What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors.Preprint

Geiping, J., Fowl, L., Somepalli, G., Goldblum, M., Moeller, M., & Goldstein, T. (2021) · arXiv (Cornell University) · Cited by 15

A comprehensive review of AI-enabled predictive maintenance for hydrogen-powered transport: Advancing process safety and sustainability

Vellaiyan, S., Aljohani, K., Aljohani, B. S. E., Kuppusamy, S., & Minh, N. V. (2026) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2026.105953 · Cited by 14

Toward a Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures

Qiu, H., Ma, H., Zhang, Z., Abuadbba, A., Kang, W., Fu, A., & Gao, Y. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3324318 · Cited by 17

Managing Cybersecurity Risks in Emerging Technologies

Yaacob, M. N., Idrus, S. Z. S., & IDRIS, M. (2023) · International Journal of Business and Technopreneurship (IJBT) · doi:10.58915/ijbt.v13i3.297 · Cited by 12

Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario

Sánchez, P. M. S., Celdrán, A. H., Pérez, E. T. M., Demeter, D., Bovet, G., Pérez, G. M., & Stiller, B. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05510-1 · Cited by 13

Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees

Nowroozi, E., Jadalla, N., Ghelichkhani, S., & Jolfaei, A. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3447411 · Cited by 12

A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks

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

Venomave: Targeted Poisoning Against Speech Recognition

Aghakhani, H., Schönherr, L., Eisenhofer, T., Kolossa, D., Holz, T., Kruegel, C., & Vigna, G. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00035 · Cited by 16

Covert Channel Attack to Federated Learning Systems.Preprint

Costa, G., Pinelli, F., Soderi, S., & Tolomei, G. (2021) · arXiv (Cornell University) · Cited by 12

Subnet Replacement: Deployment-stage backdoor attack against deep neural networks in gray-box settingPreprint

Qi, X., Jifeng, Z., Xie, C., & Yang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07240 · Cited by 28

Navigating Embodied Intelligence: Enabling Technologies, Security and Privacy, and Emerging Trends

Wang, Y., Niu, X., Ba, J., Su, Z., & Du, L. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3649049 · Cited by 11

MITDBA: Mitigating Dynamic Backdoor Attacks in Federated Learning for IoT Applications

Wang, Y., Zhai, D., Han, D., Guan, Y., & Xia, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325634 · Cited by 12

The Devil’s Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models

Dolatabadi, H. M., Erfani, S., & Leckie, C. A. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00025 · Cited by 19

Social bot detection using variational generative adversarial networks with hidden Markov models in Twitter network

Lingam, G., & Das, S. K. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113019 · Cited by 11

Defenses in Adversarial Machine Learning: A Systematic Survey From the Lifecycle Perspective

Wu, B., Zhu, M., Zheng, M., Zhu, Z., Wei, S., Zhang, M., Chen, H., Yuan, D., Liu, L., & Liu, Q. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3611340 · Cited by 11

Towards Defining a Trustworthy Artificial Intelligence System Development Maturity Model

Das, S. D., Bala, P. K., & Mishra, A. N. (2023) · Journal of Computer Information Systems · doi:10.1080/08874417.2023.2251443 · Cited by 10

Blockchain federated learning with sparsity for IoMT devices

Ba, A. F., Yingchi, M., Muhammad, A. U., Samuel, O., Muazu, T., & Kumshe, U. M. M. (2024) · Cluster Computing · doi:10.1007/s10586-024-04810-y · Cited by 11

Survey on Backdoor Attacks on Deep Learning: Current Trends, Categorization, Applications, Research Challenges, and Future Prospects

Hanif, M. A., Chattopadhyay, N., Ouni, B., & Shafique, M. (2025) · IEEE Access · doi:10.1109/access.2025.3571995 · Cited by 12

Fake the Real: Backdoor Attack on Deep Speech Classification via Voice Conversion

Ye, Z., Mao, T., Dong, L., & Yan, D. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-733 · Cited by 19

LearnChain: Transparent and cooperative reinforcement learning on Blockchain

Sami, H., Mizouni, R., Otrok, H., Singh, S., Bentahar, J., & Mourad, A. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.012 · Cited by 10

The Path to Defence: A Roadmap to Characterising Data Poisoning Attacks on Victim Models

Chaalan, T., Pang, S., Kamruzzaman, J., Gondal, I., & Zhang, X. (2023) · ACM Computing Surveys · doi:10.1145/3627536 · Cited by 13

DeepVenom: Persistent DNN Backdoors Exploiting Transient Weight Perturbations in Memories

Cai, K., Chowdhuryy, M. H. I., Zhang, Z., & Yao, F. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00223 · Cited by 12

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

Cinà, A. E., Grosse, K., Demontis, A., Vascon, S., Zellinger, W., Moser, B. A., Oprea, A., Biggio, B., Pelillo, M., & Roli, F. (2023) · ACM Computing Surveys · doi:10.1145/3585385 · Cited by 152

A study of security threats and defense methods on machine learning training datasets

Vamsi, B. (2020) · International Journal of Computing Programming and Database Management · doi:10.33545/27076636.2020.v1.i2a.11

Data poisoning: issues, challenges, and needs

Aljanabi, M., Omran, A. H., Mijwil, M. M., Abotaleb, M. S. A., El‐kenawy, E. M., Mohammed, S. Y., & Ibrahim, A. F. (2024) · IET conference proceedings. · doi:10.1049/icp.2024.0951 · Cited by 14

Machine Learning Security Against Data Poisoning: Are We There Yet?

Ciná, A. E., Grosse, K., Demontis, A., Biggio, B., Roli, F., & Pelillo, M. (2024) · Computer · doi:10.1109/mc.2023.3299572 · Cited by 47

Poisoning Attacks and Defenses on Artificial Intelligence: A SurveyPreprint

Ramirez, M. A., Kim, S.-K., Hamadi, H. A., Damiani, E., Byon, Y.-J., Kim, T., Cho, C.-S., & Yeun, C. Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.10276 · Cited by 61