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

Bird's-eye view on the Automotive Cybersecurity Landscape & Challenges in adopting AI/ML

Siddiqui, F. M., Khan, R., & Sezer, S. (2021) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec54266.2021.9732568 · Cited by 10

Robust and Transferable Backdoor Attacks Against Deep Image Compression With Selective Frequency Prior

Yu, Y., Wang, Y., Yang, W., Guo, L., Lu, S., Duan, L., Tan, Y., & Kot, A. C. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3507873 · Cited by 8

Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm

Paolone, G., Paesani, R., Pilotti, F., Camplone, J., Piazza, A., & Felice, P. D. (2025) · Future Internet · doi:10.3390/fi17020090 · Cited by 11

A DRL-Based Deviation-Aware Federated Digital Twin Construction over Wireless Edge Network

Wu, J., Chen, R., & Yi, C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8725-1_16 · Cited by 9

Learning a robust foundation model against clean-label data poisoning attacks at downstream tasks

Zhou, T., Yan, H., Han, B., Liu, L., & Zhang, J. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.034 · Cited by 10

PATCH

Wang, J., Wang, G., Zhang, X., Liu, L., Zeng, H., Xiao, L., Cao, Z., Gu, L., & Li, T. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3610885 · Cited by 11

Energy-latency attacks via sponge poisoning

Ciná, A. E., Demontis, A., Biggio, B., Roli, F., & Pelillo, M. (2025) · Information Sciences · doi:10.1016/j.ins.2025.121905 · Cited by 8

SGBA: A stealthy scapegoat backdoor attack against deep neural networks

He, Y., Shen, Z., Xia, C., Hua, J., Tong, W., & Zhong, S. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103523 · Cited by 12

Is My Data in Your AI? Membership Inference Test (MINT) Applied to Face Biometrics

DeAlcala, D., Morales, A., Fiérrez, J., Mancera, G., Tolosana, R., & Ortega-García, J. (2025) · IEEE Access · doi:10.1109/access.2025.3608951 · Cited by 9

Boosting credit risk models

Baesens, B., & Smedts, K. (2023) · The British Accounting Review · doi:10.1016/j.bar.2023.101241 · Cited by 11

Manipulating Pre-Trained Encoder for Targeted Poisoning Attacks in Contrastive Learning

Chen, J., Gao, Y., Liu, G., Abdelmoniem, A. M., & Wang, C. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3350389 · Cited by 7

Improved gradient leakage attack against compressed gradients in federated learning

Ding, X., Liu, Z., You, X., Li, X., & Vasilakos, A. V. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128349 · Cited by 9

A highly efficient, confidential, and continuous federated learning backdoor attack strategy

Cao, J., & Zhu, L. (2022) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3529836.3529845 · Cited by 7

Blockchain-based fairness-enhanced federated learning scheme against label flipping attack

Jin, S., Li, Y., Chen, X., Li, R., & Shen, Z. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103580 · Cited by 7

Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making

Mehrpour, O., Nakhaee, S., Abdollahi, J., & Vohra, V. (2025) · European Journal of Pediatrics · doi:10.1007/s00431-024-05957-x · Cited by 7

Poison to Cure: Privacy-preserving Wi-Fi Multi-User Sensing via Data Poisoning

Hu, J., Li, X., Gan, J., & Luo, J. (2025) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3680207.3723459 · Cited by 6

ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection

Alam, T., Piplai, A., & Rastogi, N. (2025) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1109/raid67961.2025.00048 · Cited by 15

Backdooring rationalization plus

Wu, L., Kong, L., He, F., Wang, B., Su, Y., Wang, X., & Jiang, X. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109556 · Cited by 7

Machine Learning for Human–Machine Systems With Advanced Persistent Threats

Chen, L., Zhang, W., Song, Y., & Chen, J. (2024) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2024.3439625 · Cited by 8

Spatialspectral-Backdoor: Realizing backdoor attack for deep neural networks in brain–computer interface via EEG characteristics

Li, F., Huang, M., You, W., Zhu, L., Cheng, H., & Yang, R. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128902 · Cited by 7

Clean-label backdoor attack based on robust feature attenuation for speech recognition

Cai, H., Zhang, P., Xiao, Y., Ji, S., Xiao, M., & Cheng, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127546 · Cited by 7

A Robust Framework for Adaptive Selection of Filter Ensembles to Detect Adversarial Inputs

Roy, A., & Dasgupta, D. (2022) · International Conference on Dependable Systems and Networks workshops · doi:10.1109/dsn-w54100.2022.00019 · Cited by 6

Edge-Cloud Collaborative Defense against Backdoor Attacks in Federated Learning

Yang, J., Zheng, J., Wang, H., Li, J., Sun, H., Han, W., Jiang, N., & Tan, Y. (2023) · Sensors · doi:10.3390/s23031052 · Cited by 6

Federated learning security mechanisms for protecting sensitive data

Al-Ameer, A. A. A., & Bhaya, W. S. (2023) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/beei.v12i4.4751 · Cited by 7

A lightweight backdoor defense framework based on image inpainting

Wei, Y., Gao, H., Wang, Y., Gao, Y., & Liu, H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.03.052 · Cited by 6

FLARE: A Backdoor Attack to Federated Learning with Refined Evasion

Wang, Q., Wu, Y., Xuan, H., & Wu, H. (2024) · Mathematics · doi:10.3390/math12233751 · Cited by 5

Improved Distributed Backdoor Attacks in Federated Learning by Density-Adaptive Data Poisoning and Projection-Based Gradient Updating

Wang, J., Shen, H., Ke, W., & Liu, X. H. (2025) · IEEE Access · doi:10.1109/access.2025.3586416 · Cited by 5

SITA: Structurally Imperceptible and Transferable Adversarial Attacks for Stylized Image Generation

Kang, J., Yang, H., Cai, Y., Zhang, H., Xu, X., Du, Y., & He, S. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3555552 · Cited by 5

The role of open-source LLMs in shaping the future of GeoAI

Huang, X., Tu, Z., Ye, X., & Goodchild, M. F. (2026) · Annals of GIS · doi:10.1080/19475683.2026.2630753 · Cited by 8

Security and privacy in LLMs: A comprehensive survey of threats and mitigation strategies

Berini, A. D. E., Jamil, N., Benrazek, A., Lakas, A., Ismail, L., Ferrag, M. A., & Lam, K. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104241 · Cited by 14

Breaking Speaker Recognition with Paddingback

Ye, Z., Yan, D., Li, D., & Shen, K. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448169 · Cited by 5

A Robust Countermeasures for Poisoning Attacks on Deep Neural Networks of Computer Interaction Systems

Liu, I., Li, J.-S., Peng, Y.-C., & Liu, C.-G. (2022) · Applied Sciences · doi:10.3390/app12157753 · Cited by 6

Poisons that are learned faster are more effective

Sandoval-Segura, P., Singla, V., Fowl, L., Geiping, J., Goldblum, M., Jacobs, D. W., & Goldstein, T. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00033 · Cited by 6

The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022

Parry, J., Costello, D. H., Rupert, J., & Taylor, G. (2023) · Systems Engineering · doi:10.1002/sys.21703 · Cited by 4

Verifiable Machine Learning Models in Industrial IoT via Blockchain

Stodt, J., Ghajar, F. G., Reich, C., & Clarke, N. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35644-5_6 · Cited by 5

Backdoor learning curves: explaining backdoor poisoning beyond influence functions

Ciná, A. E., Grosse, K., Vascon, S., Demontis, A., Biggio, B., Roli, F., & Pelillo, M. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02363-5 · Cited by 14

A GAN-based Adversarial Attack Method for Data-driven State Estimation

Zhang, J., Hu, J., Miao, C., & Wang, Q. (2023) · IEEE International Electrical and Energy Conference (CIEEC) · doi:10.1109/cieec58067.2023.10165834 · Cited by 5

Accountable federated learning against local poisoning attacks

Wang, Y., Gao, Y., Zhang, Y., Geng, H., & Chi, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114828 · Cited by 4

An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face

Stalnaker, T., Wintersgill, N., Chaparro, O., Heymann, L. A., Penta, M. D., Germán, D. M., & Poshyvanyk, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3776739 · Cited by 4

When AI reviews science: Can we trust the referee?

Wang, J., Liu, Y., Xu, H., Hu, K., Di, S., Ni, W., Yue, L., Zhang, M.-L., Ren, K., & Chen, L. (2026) · The Innovation Informatics · doi:10.59717/j.xinn-inform.2026.100030 · Cited by 7

Artificial Intelligence System Risk Management Methodology Based on Generalized Blueprints

Bogdanov, D., Etti, P., Kamm, L., & Stomakhin, F. (2024) · International Conference on Cyber Conflict · doi:10.23919/cycon62501.2024.10685644 · Cited by 4

Analyzing Threat Level of the Backdoor Attack Method for an Organization’s Operation

Nasharuddin, M. Z. S. M., & Ibrahim, A. A. (2024) · International Journal on Perceptive and Cognitive Computing · doi:10.31436/ijpcc.v10i2.484 · Cited by 4

Attack To Defend: Exploiting Adversarial Attacks for Detecting Poisoned Models

Fares, S., & Nandakumar, K. (2024) · 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/cvpr52733.2024.02335 · Cited by 4

A CGAN-based adversarial attack method for data-driven state estimation

Wang, Q., Zhang, J., Hu, J., Wu, S., Hou, S., & Tang, Y. P. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.110878 · Cited by 4

Transferability in data poisoning attacks on spatiotemporal traffic forecasting models

Wang, X., Wang, F., Hong, Y., & Ban, X. J. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105501 · Cited by 5

COLLIDER: A Robust Training Framework for Backdoor Data

Dolatabadi, H. M., Erfani, S., & Leckie, C. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26351-4_41 · Cited by 4

Towards Audit Requirements for AI-Based Systems in Mobility Applications

Alagarswamy, D. P., Berghoff, C., Danos, V., Langer, F., Markert, T., Schneider, G., von Twickel, A., & Woitschek, F. (2023) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0011619500003405 · Cited by 4

Blockchain-Based Fairness-Enhanced Federated Learning Scheme Against Data Poisoning Attack

Jin, S., Li, Y., Chen, X., Li, R., & Shen, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28124-2_31 · Cited by 4

Disrupting Model Training with Adversarial ShortcutsPreprint

Evtimov, I., Covert, I., Kusupati, A., & Kohno, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06654 · Cited by 4

USB-GATE: USB-based GAN-augmented transformer reinforced defense framework for adversarial keystroke injection attacks

Chillara, A. K., Saxena, P., & Maiti, R. R. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-00997-2 · Cited by 3

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

Defending Backdoor Attacks in Task-Oriented Multi-User Semantic Communication: A Contrastive Training Approach

Peng, J., Xing, H., Xu, L., Luo, S., Dai, P., Feng, L., Ale, L., Zhao, B., & Xiao, Z. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3666663 · Cited by 3

A Data-Centric Anomaly-Based Detection System for Interactive Machine Learning Setups

Bugeja, J., & Persson, J. (2022) · International Conference on Web Information Systems and Technologies · doi:10.5220/0011560100003318 · Cited by 3

Defending Data Poisoning Attack through Watermarked Friendly Noise Luminosity Activated Dense Layered UNet for Classification of Lung Disease

Devi, M. S., & Saravanan, P. (2024) · IEEE Access · doi:10.1109/access.2024.3513485 · Cited by 4

Restricted Boltzmann machine with Sobel filter dense adversarial noise secured layer framework for flower species recognition

Shyamala Devi, M., Balasubaramanian, S., Balasubramaniam, S., & Shah, M. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-95564-z · Cited by 3

A Universal Identity Backdoor Attack against Speaker Verification based on Siamese Network

Zhao, H., Du, W. D., Guo, J., & Liu, G. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-446 · Cited by 6

Machine Learning Robustness in Predictive Maintenance Under Adversarial Attacks

Dionisopoulos, N., Vrochidou, Ε., & Papakostas, G. A. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-5180-2_21 · Cited by 3

Predicting IoT Distributed Ledger Fraud Transactions With a Lightweight GAN Network

Rawlins, C. C., & Jagannathan, S. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3339384 · Cited by 3

A Systematic Review of Recent Research on Chatbots

Elhiny, L., Ye, X., Speidel, U., & Manoharan, S. (2023) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce61019.2023.10442532 · Cited by 3

Improved Robust Adversarial Model against Evasion Attacks on Intrusion Detection Systems

Anaedevha, R. N., & Trofimov, A. G. (2024) · Optical Memory and Neural Networks · doi:10.3103/s1060992x24700681 · Cited by 3

Does Federated Learning Improve the Resiliency of Machine Learning Models Against Data Fault Attacks?

Tavasoli, M., Sarrafzadeh, A., & Patooghy, A. (2025) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip64458.2025.10898123 · Cited by 3

With False Friends Like These, Who Can Notice Mistakes?

Tao, L., Feng, L., Yi, J., & Chen, S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20822 · Cited by 4

A Stealthy Backdoor Attack for Code ModelsPreprint

qu, Y., Huang, S., Chen, X., Ju, X., Li, L., Wang, X., Yao, Y., & Xiao, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-3969016/v1 · Cited by 3

Advancing Security in Software-Defined Vehicles: A Comprehensive Survey and TaxonomyPreprint

Sghaier, K., Hammi, B., Gharbi, G., Merdrignac, P., Parrend, P., & Verna, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5634455 · Cited by 3

Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks

Li, A., Zhou, Y., Raghuram, V. C., Goldstein, T., & Goldblum, M. (2025) · arXiv · doi:10.48550/arxiv.2502.08586 · 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

AI-Driven Social Media Analysis for Disaster Situational Awareness in Smart Urban Environments

Singh, R., Manoharan, G., Singh, S., & Singh, S. (2025) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/979-8-3693-7832-8.ch007 · Cited by 2

Data Anonymization and Privacy Preservation in Healthcare Systems

Datta, K., & Gupta, H. (2025) · EPJ Web of Conferences · doi:10.1051/epjconf/202532801069 · Cited by 2

Superior resilience to poisoning and amenability to unlearning in quantum machine learning

Chen, Y., & Zhang, S. (2026) · Nature Communications · doi:10.1038/s41467-026-70420-4 · Cited by 6

Evaluating and Defending Against Adversarial Attacks on LLM-Generated LSTM Models

Ghahremani, M., Podshyvalin, A., & Taheri, R. (2026) · Springer eBooks · doi:10.1007/978-3-031-99447-0_13 · Cited by 2

Defense against data poisoning attacks in robot vision systems based on adversarial example detection

Chu, R., Fu, X., Luo, B., Shi, J., & Zhou, X. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-025-50195-5 · Cited by 2

An enhanced backdoor attack using a backdoor trigger position searching algorithm for avoiding deep learning-based object detection systems

Jo, S., & Cho, Y. (2026) · Cybersecurity · doi:10.1186/s42400-025-00477-z · Cited by 3

Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks

Liu, S., Cullen, A. C., Montague, P., Erfani, S., & Rubinstein, B. I. P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.26065 · Cited by 4

DeepDefense: A Steganalysis-Based Backdoor Detecting and Mitigating Protocol in Deep Neural Networks for AI Security

Zhang, L., Peng, Y., Wei, L., Chen, C., & Zhang, X. (2023) · Security and Communication Networks · doi:10.1155/2023/9308909 · Cited by 2

Enhancing Algorithmic Resilience Against Data Poisoning Using CNN

Jayapradha, J., Vadhanie, L., Kulkarni, Y., Kumar, T. S., & Uma Devi, M. (2024) · Advances in IT standards and standardization research (AISSR) book series/Advances in IT standards and standardization research series · doi:10.4018/979-8-3693-2691-6.ch008 · Cited by 2

Real Time Poisoning Attacks and Privacy Strategies on Machine Learning Systems

Archa, A. T., & Kartheeban, K. (2024) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl61749.2024.00036 · Cited by 2

Concepts and Measures Towards Trustworthy AI in Industrial Manufacturing

Zelba, F., Balzereit, K., & Windmann, S. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10710991 · Cited by 2

Defusing Dirty Label Backdoor Attack With Differentially Private Data Through Feature Vectorized AlexNet for Face Gender Classification

Devi, M. S., Priya, S., Singh, S., & Aruna, R. (2024) · IEEE Access · doi:10.1109/access.2024.3485804 · Cited by 2

Securing Machine Learning Against Data Poisoning Attacks

Allheeib, N. (2024) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.358335 · Cited by 4

A Novel Data Sanitization Method Based on Dynamic Dataset Partition and Inspection Against Data Poisoning Attacks

Lee, J., Cho, Y., Lee, R., Yuk, S., Youn, J., Park, H., & Shin, D. (2025) · Electronics · doi:10.3390/electronics14020374 · Cited by 5

Analyzing the Vulnerabilities of Targets in Clean-Label Data Poisoning Attacks

Jin, Y., Yang, X., Li, J., Pu, R., Wang, Y., & Wang, B. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-1093-8_12 · Cited by 2

Securing ML Models on Websites: Face Recognition and Spoof Detection via IPFS Blockchain

Ramakanth Kumar, P., Jambur, P. V., Kulkarni, N. V., Kapini, N. S. R., Niranjan, R., Mathew, N. S., & Kheria, P. (2025) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee64140.2025.10915410 · 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

UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning

Wang, Z., Le, T., & Lee, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.800 · Cited by 3

Learning and Certification under Instance-targeted PoisoningPreprint

Gao, J., Karbasi, A., & Mahmoody, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.08709 · Cited by 3

HOLMES: A Platform for Detecting Malicious Inputs in Secure Collaborative ComputationPreprint

Chen, W., Sotiraki, K., Chang, I., Kantarcıoğlu, M., & Popa, R. A. (2021) · IACR Cryptology ePrint Archive · Cited by 2

METHODS OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN THE FIELD OF INFORMATION SECURITY: ANALYSIS OF THE CURRENT STATE AND PROSPECTS FOR DEVELOPMENT

Ruchay, A. N., Tokarev, I. V., & Gribachev, A. S. (2022) · Journal of the Ural Federal district Information security · doi:10.14529/secur220409 · Cited by 1

Mitigating Gradient-Based Data Poisoning Attacks on Machine Learning Models: A Statistical Detection Method

Sanapala, L., & Lakshmeeswari, G. (2024) · Indian Journal of Science and Technology · doi:10.17485/ijst/v17i21.1035 · Cited by 1

Regulated Federated Learning Against the Effects of Heterogeneity and Client Attacks

Hu, F., Zhou, W., Liao, K., Li, H., & Tong, D. (2024) · IEEE Intelligent Systems · doi:10.1109/mis.2024.3410372 · Cited by 1

Repairing Backdoor Model With Dynamic Gradient Clipping for Intelligent Vehicles

Ma, X., Li, X., Zhang, J., Ma, Z., Jiang, Q. G., Liu, X., & Ma, J. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3419040 · Cited by 2

Flatness-Aware Sequential Learning Generates Resilient Backdoors

Pham, H., Ta, A. T., Tran, A., & Doan, K. D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73021-4_6 · Cited by 1

Addressing Poisoning Attacks Against Federated Intrusion Detection Systems in Internet of Healthcare Things

Boumediri, T., Debicha, I., & Kenaza, T. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-71848-9_25 · Cited by 1

Towards Privacy-Preserving and Secure Machine Unlearning: Taxonomy, Challenges and Research Directions

Tang, L., & Joshi, J. (2024) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa62245.2024.00040 · Cited by 2

Towards Robust Speech Models: Mitigating Backdoor Attacks via Audio Signal Enhancement and Fine-Pruning Techniques

Sun, H., Zhong, Q., Qi, M., Fang, U., Shi, G., & Cui, S. (2025) · Mathematics · doi:10.3390/math13060984 · Cited by 1

Graph neural networks adversarial attacks based on node gradient and importance score

Sun, Y., Ju, J., Cheng, S., Yang, J., Cao, Y., Zhang, A., & Shi, W. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122168 · Cited by 1

SecureGaze: Defending Gaze Estimation Against Backdoor Attacks

Du, L., Liu, Y., Jia, J., & Lan, G. (2025) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3715014.3722071 · Cited by 1

SMLaaS: Secure Machine Learning as a Service Ensuring Data and Model Parameter Privacy

Reddy, B. P. K., & Chatterjee, A. (2025) · Security and Privacy · doi:10.1002/spy2.70047 · Cited by 1

Model Residuals as Shields: A Two-Level Formulation to Defend Smart Grids From Poisoning Attacks

Lin, T.-W., Roy, P., Zeng, Y., Jin, M., Jia, R., Liu, C., & Sangiovanni-Vincentelli, A. L. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3575005 · Cited by 1

Foundation of secured edge intelligence in CPS

Tian, P., Liao, W., Qian, C., Guo, Y., & Yu, W. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-326572-3.00021-8 · Cited by 1

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