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 · see citing works

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

Auto-Encoding Variational Bayes

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

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 19,268 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Robust estimation of a location parameter

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

Robust Statistics

Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066 · see citing works

Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · see citing works

mixup: Beyond Empirical Risk Minimization

Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv · doi:10.48550/arxiv.1710.09412 · Cited by 5,831 · see citing works

CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features

Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00612 · Cited by 5,400 · see citing works

LOF: identifying density-based local outliers

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149 · see citing works

Procedures for Detecting Outlying Observations in Samples

Grubbs, F. E. (1969) · Technometrics · doi:10.1080/00401706.1969.10490657 · Cited by 4,105 · see citing works

Robust Statistics: The Approach Based on Influence Functions

Ruppert, D. (1987) · Technometrics · doi:10.2307/1269782 · Cited by 3,880 · see citing works

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works

Universal Adversarial Perturbations

Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., & Frossard, P. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.17 · Cited by 2,680 · see citing works

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 · see citing works

Deep Leakage from Gradients

Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906 · see citing works

EMNIST: Extending MNIST to handwritten letters

Cohen, G., Afshar, S., Tapson, J. C., & André, V. S. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966217 · Cited by 1,782 · see citing works

Robust Statistics: The Approach Based on Influence Functions.

Jennison, C., Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1987) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2981480 · Cited by 1,753 · see citing works

Adversarial Examples: Attacks and Defenses for Deep Learning

Yuan, X., He, P., Zhu, Q., & Li, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2886017 · Cited by 1,733 · see citing works

Evasion attacks against machine learning at test time

Biggio, B., Corona, I., Maiorca, D., Nelson, B. A., Srndic, N., Laskov, P., Giacinto, G., & Roli, F. (2013) · Cited by 1,658 · see citing works

Understanding Black-box Predictions via Influence Functions

Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1703.04730 · Cited by 1,514 · see citing works

How to backdoor federated learning

Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495 · see citing works

Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks

Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H.-T., & Zhao, B. Y. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00031 · Cited by 1,486 · see citing works

Empirical Risk Minimization

Zhang, X. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_79 · Cited by 1,437 · see citing works

Untitled work

(2017) · neural information processing systems · Cited by 1,413 · see citing works

Poisoning Attacks against Support Vector Machines

Biggio, B., Nelson, B., & Laskov, P. (2012) · arXiv · doi:10.48550/arxiv.1206.6389 · Cited by 1,331 · see citing works

Delving into Transferable Adversarial Examples and Black-box Attacks

Liu, Y., Chen, X., Liu, C., & Song, D. (2016) · arXiv · doi:10.48550/arxiv.1611.02770 · Cited by 1,310 · see citing works

Trojaning Attack on Neural Networks

Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., & Zhang, X. (2018) · Proceedings 2018 Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23291 · Cited by 1,270 · see citing works

Certified Adversarial Robustness via Randomized SmoothingPreprint

Cohen, J. M., Rosenfeld, E., & Kolter, J. Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.02918 · Cited by 1,172 · see citing works

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Yin, D., Chen, Y., Ramchandran, K., & Bartlett, P. (2018) · arXiv · doi:10.48550/arxiv.1803.01498 · Cited by 1,130 · see citing works

BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Gu, T., Dolan-Gavitt, B., & Garg, S. (2017) · arXiv · doi:10.48550/arxiv.1708.06733 · Cited by 1,061 · see citing works

Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

Liu, K., Dolan-Gavitt, B., & Garg, S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00470-5_13 · Cited by 1,055 · see citing works

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 · see citing works

Synthesizing Robust Adversarial ExamplesPreprint

Athalye, A., Engstrom, L., Ilyas, A., & Kwok, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07397 · Cited by 973 · see citing works

Robust Aggregation for Federated Learning

Pillutla, K., Kakade, S. M., & Harchaoui, Z. (2022) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2022.3153135 · Cited by 947 · see citing works

Data Poisoning Attacks Against Federated Learning Systems

Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58951-6_24 · Cited by 927 · see citing works

Procedures for Detecting Outlying Observations in Samples

Grubbs, F. E. (1969) · Technometrics · doi:10.2307/1266761 · Cited by 907 · see citing works

LEAF: A Benchmark for Federated Settings

Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1812.01097 · Cited by 894 · see citing works

Evasion Attacks against Machine Learning at Test Time

Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., & Roli, F. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40994-3_25 · Cited by 890 · see citing works

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2018.00057 · Cited by 861 · see citing works

General notions of statistical depth function

Serfling, R. J., & Zuo, Y. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218226 · Cited by 845 · see citing works

Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv · doi:10.48550/arxiv.1911.11815 · Cited by 828 · see citing works

Certified Robustness to Adversarial Examples with Differential Privacy

Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., & Jana, S. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00044 · Cited by 750 · see citing works

Inverting Gradients -- How easy is it to break privacy in federated learning?Preprint

Geiping, J., Bauermeister, H., Dröge, H., & Moeller, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14053 · Cited by 749 · see citing works

STRIP

Gao, Y., Xu, C., Wang, D., Chen, S., Ranasinghe, D. C., & Nepal‬, ‪. (2019) · Annual Computer Security Applications Conference · doi:10.1145/3359789.3359790 · Cited by 719 · see citing works

Embedding Watermarks into Deep Neural Networks

Uchida, Y., Nagai, Y., Sakazawa, S., & Satoh, S. (2017) · International Conference on Multimedia Retrieval · doi:10.1145/3078971.3078974 · Cited by 709 · see citing works

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 · see citing works

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 · see citing works

The Space of Transferable Adversarial ExamplesPreprint

Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017) · arXiv.org · doi:10.48550/arxiv.1704.03453 · Cited by 649 · see citing works

A survey of sampling from contaminated distributions

Tukey, J. W. (1960) · Medical Entomology and Zoology · Cited by 645 · see citing works

Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural NetworksPreprint

Shafahi, A., Huang, W., Najibi, M., Suciu, O., Studer, C., Dumitraş, T., & Goldstein, T. (2018) · arXiv (Cornell University) · doi:10.3929/ethz-b-000460348 · Cited by 626 · see citing works

Robust regression using repeated medians

Siegel, A. F. (1982) · Biometrika · doi:10.1093/biomet/69.1.242 · Cited by 580 · see citing works

Protecting Intellectual Property of Deep Neural Networks with Watermarking

Zhang, J., Gu, Z., Jang, J., Wu, H., Stoecklin, M. P., Huang, H., & Molloy, I. (2018) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3196494.3196550 · Cited by 556 · see citing works

Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization

Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., & Roli, F. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140451 · Cited by 549 · see citing works

Spectral Signatures in Backdoor AttacksPreprint

Tran, B., Li, J. Z., & Mądry, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00636 · Cited by 506 · see citing works

SoK: Security and Privacy in Machine Learning

Papernot, N., McDaniel, P., Sinha, A., & Wellman, M. P. (2018) · 2018 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2018.00035 · Cited by 502 · see citing works

Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

Chen, B., Carvalho, W., Baracaldo, N., Ludwig, H., Edwards, B., Lee, T., Molloy, I., & Srivastava, B. (2018) · arXiv · doi:10.48550/arxiv.1811.03728 · Cited by 485 · see citing works

Certified Defenses for Data Poisoning Attacks

Steinhardt, J., Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1706.03691 · Cited by 479 · see citing works

DBA: Distributed Backdoor Attacks against Federated Learning

Xie, C., Huang, K., Chen, P., & Li, B. (2020) · International Conference on Learning Representations · Cited by 468 · see citing works

Outliers in statistical data

Barnett, V., & Lewis, T. (1974) · Cited by 446 · see citing works

Threats to Federated Learning

Lyu, L., Yu, H., Zhao, J., & Yang, Q. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_1 · Cited by 444 · see citing works

Synthesizing Robust Adversarial Examples

Athalye, A., Engstrom, L., Ilyas, A., & Kwok, K. (2018) · International Conference on Machine Learning · Cited by 432 · see citing works

SoK: Towards the Science of security and privacy in machine learning

Papernot, N., McDaniel, P. D., Sinha, A., & Wellman, M. P. (2018) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 427 · see citing works

Attack of the tails: Yes, you really can backdoor federated learning

Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-Y., Lee, K., & Papailiopoulos, D. (2020) · arXiv · doi:10.48550/arxiv.2007.05084 · Cited by 406 · see citing works

Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by BackdooringPreprint

Adi, Y., Baum, C., Cissé, M., Benny Pinkas, B., & Keshet, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04633 · Cited by 395 · see citing works

RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets

Li, L., Xu, W., Chen, T., Giannakis, G. B., & Ling, Q. H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011544 · Cited by 392 · see citing works

Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent

Chen, Y., Su, L., & Xu, J. (2017) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3154503 · Cited by 390 · see citing works

Mitigating Sybils in Federated Learning Poisoning

Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2018) · arXiv · doi:10.48550/arxiv.1808.04866 · Cited by 385 · see citing works

Latent Backdoor Attacks on Deep Neural Networks

Yao, Y., Li, H., Zheng, H.-T., & Zhao, B. Y. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3354209 · Cited by 370 · see citing works

Can You Really Backdoor Federated Learning?

Sun, Z., Kairouz, P., Suresh, A. T., & McMahan, H. B. (2019) · arXiv · doi:10.48550/arxiv.1911.07963 · Cited by 348 · see citing works

Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners

Mei, S., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9569 · Cited by 346 · see citing works

Learning in the Presence of Malicious Errors

Kearns, M. J., & Li, M. (1993) · SIAM Journal on Computing · doi:10.1137/0222052 · Cited by 340 · see citing works

Outlier Detection: Methods, Models, and Classification

Boukerche, A., Zheng, L., & Alfandi, O. (2021) · ACM Computing Surveys · doi:10.1145/3381028 · Cited by 334 · see citing works

Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

Li, S., Xue, M., Zhao, B. Z. H., Zhu, H., & Zhang, X. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.3021407 · Cited by 325 · see citing works

Exploiting machine learning to subvert your spam filter

Nelson, B. A., Barreno, M., Chi, F. J., Joseph, A. D., Rubinstein, B. I. P., Saini, U., Sutton, C. A., Tygar, J. D., & Xia, K. (2008) · Cited by 322 · see citing works

Support Vector Machines Under Adversarial Label Noise

Biggio, B., Nelson, B. A., & Laskov, P. (2011) · UNICA IRIS Institutional Research Information System (University of Cagliari) · Cited by 319 · see citing works

Untitled work

(2019) · neural information processing systems · Cited by 318 · see citing works

Is feature selection secure against training data poisoning?Preprint

Huang, X. X., Biggio, B., Gavin Brown, B., Fumera, G., Eckert, C. M., & Roli, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.07933 · Cited by 314 · see citing works

DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks

Chen, H., Fu, C., Zhao, J., & Koushanfar, F. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/647 · Cited by 314 · see citing works

Data Poisoning Attacks on Federated Machine Learning

Sun, G., Cong, Y., Dong, J., Wang, Q., Lyu, L., & Liu, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3128646 · Cited by 310 · see citing works

A Backdoor Attack Against LSTM-Based Text Classification Systems

Dai, J., Chen, C., & Li, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2941376 · Cited by 298 · see citing works

Robust Statistics

Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (2005) · Wiley series in probability and statistics · doi:10.1002/9781118186435 · Cited by 295 · see citing works

Understanding Distributed Poisoning Attack in Federated Learning

Cao, D., Chang, S., Lin, Z., Liu, G., & Sun, D. (2019) · 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads47876.2019.00042 · Cited by 285 · see citing works

SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems

Chou, E., Tramer, F., & Pellegrino, G. (2020) · 2020 IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw50608.2020.00025 · Cited by 281 · see citing works

Detecting AI Trojans Using Meta Neural Analysis

Xu, X., Wang, Q., Li, H., Borisov, N., Gunter, C. A., & Li, B. (2021) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40001.2021.00034 · Cited by 278 · see citing works

A taxonomy and survey of attacks against machine learning

Pitropakis, N., Panaousis, E., Giannetsos, T., Anastasiadis, E., & Loukas, G. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2019.100199 · Cited by 268 · see citing works

NIC: Detecting Adversarial Samples with Neural Network Invariant Checking

Ma, S., Liu, Y., Tao, G., Lee, W., & Zhang, X. (2019) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2019.23415 · Cited by 247 · see citing works

Neural Trojans

Liu, Y., Xie, Y., & Srivastava, A. (2017) · 2017 IEEE International Conference on Computer Design (ICCD) · doi:10.1109/iccd.2017.16 · Cited by 241 · see citing works

Data poisoning attacks on factorization-based collaborative filtering

Li, B., Wang, Y., Singh, A., & Vorobeychik, Y. (2016) · arXiv · doi:10.48550/arxiv.1608.08182 · Cited by 235 · see citing works

Outliers in Statistical Data.

David, H. A., BARNETT, V. D., & Lewis, T. (1985) · Biometrics · doi:10.2307/2530985 · Cited by 235 · see citing works

Backdoor Attacks Against Deep Learning Systems in the Physical World

Wenger, E., Passananti, J., Bhagoji, A. N., Yao, Y., Zheng, H.-T., & Zhao, B. Y. (2021) · 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/cvpr46437.2021.00614 · Cited by 233 · see citing works

Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent

Chen, Y., Su, L., & Xu, J. (2018) · Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems · doi:10.1145/3219617.3219655 · Cited by 228 · see citing works

Threats to federated learning A survey

Lyu, L., Yu, H., & Yang, Q. (2020) · arXiv · doi:10.48550/arxiv.2003.02133 · Cited by 221 · see citing works

Backdoor Attacks to Graph Neural Networks

Zhang, Z., Jia, J., Wang, B., & Gong, N. Z. (2021) · Proceedings of the 26th ACM Symposium on Access Control Models and Technologies · doi:10.1145/3450569.3463560 · Cited by 220 · see citing works

Poisoning Attack in Federated Learning using Generative Adversarial Nets

Zhang, J., Chen, J., Wu, D., Chen, B., & Yu, S. (2019) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom/bigdatase.2019.00057 · Cited by 216 · see citing works

Poisoning Attacks to Graph-Based Recommender Systems

Fang, M., Yang, G., Gong, N. Z., & Liu, J. (2018) · Annual Computer Security Applications Conference · doi:10.1145/3274694.3274706 · Cited by 211 · see citing works

Learning from untrusted data

Charikar, M., Steinhardt, J., & Valiant, G. (2017) · ACM SIGACT Symposium on Theory of Computing (STOC) · doi:10.1145/3055399.3055491 · Cited by 210 · see citing works

Agnostic Estimation of Mean and Covariance

Lai, K. A., Rao, A. B., & Vempala, S. (2016) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2016.76 · Cited by 208 · see citing works

Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive ReviewPreprint

Gao, Y., Doan, B. G., Zhang, Z., Ma, S., Zhang, J., Fu, A., Nepal‬, ‪., & Kim, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10760 · Cited by 207 · see citing works

BaFFLe: Backdoor Detection via Feedback-based Federated Learning

Andreina, S., Marson, G. A., Mollering, H., & Karame, G. (2021) · 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs51616.2021.00086 · Cited by 205 · see citing works

Learning to Detect Malicious Clients for Robust Federated LearningPreprint

Li, S., Cheng, Y. J., Wang, W., Liu, Y., & Chen, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00211 · Cited by 202 · see citing works

The "Automatic" Robustness of Minimum Distance Functionals

Donoho, D. L., & Liu, R. C. (1988) · The Annals of Statistics · doi:10.1214/aos/1176350820 · Cited by 201 · see citing works

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

Kolouri, S., Saha, A., Pirsiavash, H., & Hoffmann, H. (2020) · 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/cvpr42600.2020.00038 · Cited by 190 · see citing works

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets

Zhu, C., Huang, W., Shafahi, A., Li, H., Taylor, G., Studer, C., & Goldstein, T. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000459774 · Cited by 190 · see citing works

Stronger Data Poisoning Attacks Break Data Sanitization Defenses

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A Little Is Enough: Circumventing Defenses For Distributed LearningPreprint

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Label-Consistent Backdoor AttacksPreprint

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LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial RecognitionPreprint

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Learning to Confuse: Generating Training Time Adversarial Data with Auto-EncoderPreprint

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Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating

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Diffusion Models for Imperceptible and Transferable Adversarial Attack

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

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

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

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

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

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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 97 · see citing works

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

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

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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 78 · see citing works

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

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

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Property Inference from Poisoning

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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 63 · see citing works

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

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

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Industry-Scale Orchestrated Federated Learning for Drug Discovery

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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 60 · see citing works

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

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Data poisoning attacks in intelligent transportation systems: A survey

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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 · see citing works

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

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Backdoors in Neural Models of Source Code

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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 50 · see citing works

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 38 · see citing works

Untargeted Backdoor Attack Against Object Detection

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

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

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Backdooring Multimodal Learning

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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 33 · see citing works

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 32 · see citing works

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

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

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Backdoor Attacks Against Dataset Distillation

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

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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 · see citing works

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

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Artificial Intelligence in Renewable Energy Systems: Applications and Security Challenges

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A Survey on Trusted Distributed Artificial Intelligence

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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 29 · see citing works

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 28 · see citing works

An adaptive robust defending algorithm against backdoor attacks in federated learning

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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 31 · see citing works

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 · see citing works

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 24 · see citing works

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 · see citing works

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

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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 · see citing works

Blockchain for Security in Digital Twins

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Turning Federated Learning Systems Into Covert Channels

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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 · see citing works

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

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Holistic Adversarial Robustness of Deep Learning Models

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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 · see citing works

Cybersecurity and privacy in smart bioprinting

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

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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 17 · see citing works

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

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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 17 · see citing works

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 16 · see citing works

A decentralized data evaluation framework in federated learning

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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 · see citing works

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

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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 14 · see citing works

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 · see citing works

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 14 · see citing works

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

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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 13 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 12 · see citing works

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 · see citing works

Covert Channel Attack to Federated Learning Systems.Preprint

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

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 12 · see citing works

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 · see citing works

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 11 · see citing works

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 11 · see citing works

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

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

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Towards Defining a Trustworthy Artificial Intelligence System Development Maturity Model

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Blockchain federated learning with sparsity for IoMT devices

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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 11 · see citing works

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

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LearnChain: Transparent and cooperative reinforcement learning on Blockchain

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

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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 10 · see citing works

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 · see citing works

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

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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 · see citing works

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

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Poisoning Attacks and Defenses on Artificial Intelligence: A SurveyPreprint

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