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

Robust Statistics

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

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

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

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

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

Procedures for Detecting Outlying Observations in Samples

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

Robust Statistics: The Approach Based on Influence Functions

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

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

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

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 2,410

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

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

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

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

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

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 1,577

Understanding Black-box Predictions via Influence Functions

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

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

Empirical Risk Minimization

Zhang, X. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_79 · Cited by 1,476

Untitled work

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

Poisoning Attacks against Support Vector Machines

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

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

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

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 1,232

Synthesizing Robust Adversarial ExamplesPreprint

Athalye, A., Engstrom, L., Ilyas, A., & Kwok, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07397 · Cited by 1,133

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

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 1,068

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

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

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

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

Procedures for Detecting Outlying Observations in Samples

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

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

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

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 886

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

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 848

General notions of statistical depth function

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

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

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 752

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

A survey of sampling from contaminated distributions

Tukey, J. W. (1960) · Medical Entomology and Zoology · Cited by 736

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

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

The Hidden Vulnerability of Distributed Learning in Byzantium

Mhamdi, E. M. E., Guerraoui, R., & Rouault, S. (2018) · arXiv · doi:10.48550/arxiv.1802.07927 · Cited by 648

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

The Space of Transferable Adversarial ExamplesPreprint

Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.03453 · Cited by 589

Robust regression using repeated medians

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

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 577

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

Spectral Signatures in Backdoor AttacksPreprint

Tran, B., Li, J. Z., & Mądry, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00636 · Cited by 529

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

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

Certified Defenses for Data Poisoning Attacks

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

DBA: Distributed Backdoor Attacks against Federated Learning

Xie, C., Huang, K., Chen, P., & Li, B. (2020) · International Conference on Learning Representations · Cited by 472

Label-Consistent Backdoor AttacksPreprint

Turner, A., Tsipras, D., & Mądry, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02771 · Cited by 447

Outliers in statistical data

Barnett, V., & Lewis, T. (1974) · Cited by 445

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

Synthesizing Robust Adversarial Examples

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

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

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

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 403

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

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

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

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 382

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

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

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 346

Learning in the Presence of Malicious Errors

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

Outlier Detection: Methods, Models, and Classification

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

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

Untitled work

(2019) · neural information processing systems · Cited by 318

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 318

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 315

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 · cited 3×

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 313

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

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 309

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 306

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

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

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 284

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

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 276

Stronger data poisoning attacks break data sanitization defenses

Koh, P. W., Steinhardt, J., & Liang, P. (2021) · Machine Learning · doi:10.1007/s10994-021-06119-y · Cited by 257

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 252

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 249

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

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 237

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

Outliers in Statistical Data.

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

Learning from untrusted data

Charikar, M., Steinhardt, J., & Valiant, G. J. (2017) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3055399.3055491 · Cited by 234

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

Threats to federated learning A survey

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

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

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 217

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

Generative Poisoning Attack Method Against Neural NetworksPreprint

Yang, C., Wu, Q., Li, H. H., & Chen, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01340 · Cited by 215

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

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 204

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

TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI SystemsPreprint

Guo, W., Wang, L., Xing, X., Du, M., & Song, D. X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01763 · Cited by 199

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 196

Stronger Data Poisoning Attacks Break Data Sanitization Defenses

Koh, P. W., Steinhardt, J., & Liang, P. (2018) · arXiv · doi:10.48550/arxiv.1811.00741 · Cited by 189

Analyzing Federated Learning through an Adversarial Lens

Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. B. (2019) · International Conference on Machine Learning · Cited by 188

Fawkes: Protecting Privacy against Unauthorized Deep Learning ModelsPreprint

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An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks

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Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionPreprint

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Model-Reuse Attacks on Deep Learning Systems

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Backdoor Attacks Against Transfer Learning With Pre-Trained Deep Learning Models

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Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks

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BadNL: Backdoor Attacks Against NLP ModelsPreprint

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You Autocomplete Me: Poisoning Vulnerabilities in Neural Code CompletionPreprint

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Subpopulation Data Poisoning Attacks

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Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems

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TensorClog: An Imperceptible Poisoning Attack on Deep Neural Network Applications

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Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating

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Data Poisoning Attacks in Contextual Bandits

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Analysis of Causative Attacks against SVMs Learning from Data Streams

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Face-Off: Adversarial Face Obfuscation

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Online Data Poisoning Attacks

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