Fetching the paper…
Reading the bibliography…
The recent success of machine learning (ML) has been fueled by the increasing availability of computing power and large amounts of data in many different applications.
J. H. Saltzer and M. D. Schroeder, “The protection of information in computer systems,” Proc. IEEE , 1975
1975
Earlier work this paper cites.
B. Nelson, M. Barreno, F. J. Chi, A. D. Joseph, B. I. P. Rubinstein, U. Saini, C. Sutton, J. D. Tygar, and K. Xia, “Exploiting machine learning to subvert your spam filter,” in USENIX Workshop , 2008
2008
Earlier work this paper cites.
B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in ICML , 2012
2012
Earlier work this paper cites.
T. Gu, B. Dolan-Gavitt, and S. Garg, “Badnets: Identifying vulnerabilities in the machine learning model supply chain,” CoRR , 2017
2017
Earlier work this paper cites.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” in ICML , 2017
2017
Earlier work this paper cites.
J. Steinhardt, P. W. W. Koh, and P. S. Liang, “Certified defenses for data poisoning attacks,” NeurIPS , 2017
2017
Earlier work this paper cites.
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein, “Poison frogs! targeted clean-label poisoning attacks on neural networks,” in NeurIPS , 2018
2018
Earlier work this paper cites.
European Commission and Directorate-General for Communications Networks, Content and Technology, Ethics guidelines for trustworthy AI . Publications Office, 2019
2019
Cited alongside, same era.
A. Demontis, M. Melis, M. Pintor, M. Jagielski, B. Biggio, A. Oprea, C. Nita-Rotaru, and F. Roli, “Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks,” in USENIX Security Symposium , 2019
2019
Cited alongside, same era.
J. Feng, Q. Cai, and Z. Zhou, “Learning to confuse: Generating training time adversarial data with auto-encoder,” in NeurIPS , 2019
2019
Cited alongside, same era.
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,” in IEEE Symposium on Security and Privacy , 2019
2019
Cited alongside, same era.
W. R. Huang, J. Geiping, L. Fowl, G. Taylor, and T. Goldstein, “MetaPoison: Practical General-purpose Clean-label Data Poisoning,” in NeurIPS , 2020
2020
Later among the works it cites.
European Union Agency for Cybersecurity, ENISA,, “Securing Machine Learning Algorithms,” https://www.enisa.europa.eu/publications/securing-machine-learning-algorithms , 2021
2021
Later among the works it cites.
K. Doan, Y. Lao, W. Zhao, and P. Li, “LIRA: learnable, imperceptible and robust backdoor attacks,” in IEEE/CVF ICCV , 2021
2021
Later among the works it cites.
D. Tang, X. Wang, H. Tang, and K. Zhang, “Demon in the variant: Statistical analysis of { \{ DNNs } \} for robust backdoor contamination detection,” in USENIX Security Symposium , 2021
2021
Later among the works it cites.
A. E. Cinà, A. Demontis, B. Biggio, F. Roli, and M. Pelillo, “Energy-latency attacks via sponge poisoning,” arXiv , 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. S. S. Kumar, M. Nyström, J. Lambert, A. Marshall, M. Goertzel, A. Comissoneru, M. Swann, and S. Xia, “Adversarial machine learning-industry perspectives,” in IEEE Security and Privacy Workshops , 2020
2020
Cited alongside, same era.
M. Jagielski, G. Severi, N. P. Harger, and A. Oprea, “Subpopulation Data Poisoning Attacks,” ACM SIGSAC Conference on Computer and Communications Security , 2020
2020
Cited alongside, same era.
S. Hong, V. Chandrasekaran, Y. Kaya, T. Dumitraş, and N. Papernot, “On the effectiveness of mitigating data poisoning attacks with gradient shaping,” arXiv , 2020
2020
Cited alongside, same era.
2022
Closest in time.
A. E. Cinà, K. Grosse, A. Demontis, S. Vascon, W. Zellinger, B. A. Moser, A. Oprea, B. Biggio, M. Pelillo, and F. Roli, “Wild patterns reloaded: A survey of machine learning security against training data poisoning,” ACM Computing Surveys , 2023
2023
Closest in time.