Open Access

Adversarial Learning Under Noise And Weak Supervision: Robust Methodological Foundations And Applications Across Security, Perception, And Socio-Technical Systems

PDF

Rahul Chatterjee ¹ ,

⁴ Department of Computer Science, University of Melbourne, Australia

Abstract

Adversarial learning has emerged as a unifying paradigm across machine learning, security, perception, and complex socio-technical systems, particularly in environments characterized by noisy labels, weak supervision, and strategic manipulation. This article develops a comprehensive and theoretically grounded synthesis of adversarial learning under noise, drawing strictly from foundational and contemporary literature spanning noisy example learning, adversarial label learning, generative adversarial networks, weak supervision, and adversarial robustness in applied domains such as network intrusion detection, medical signal analysis, and urban traffic systems. The study advances an integrated conceptual framework that treats noise, adversarial behavior, and supervision uncertainty not as isolated challenges but as structurally related phenomena that shape learning dynamics. Through extensive methodological exposition, the article explicates how stochastic adversarial labels, weak supervision frameworks, and adversarial training objectives interact with distributional distances, transparency mechanisms, and robustness constraints. The results are presented as a detailed descriptive synthesis of theoretical and empirical findings reported in the literature, emphasizing patterns, trade-offs, and emergent properties rather than numerical outcomes. The discussion critically examines limitations in current adversarial learning approaches, including scalability, interpretability, and domain transferability, while outlining future research trajectories that bridge probabilistic learning theory, adversarial security analysis, and real-world deployment. By offering an exhaustive elaboration of adversarial learning under noise, this work contributes a publication-ready reference that consolidates fragmented insights into a coherent methodological and conceptual foundation for robust machine learning in adversarial environments.

Keywords

Adversarial learning, noisy labels, weak supervision

References

Dana Angluin and Philip Laird. Learning from noisy examples. Machine Learning, 2(4), 343–370, 1988.

Chidubem Arachie and Bert Huang. Stochastic generalized adversarial label learning. arXiv preprint arXiv:1906.00512, 2019.

Chidubem Arachie and Bert Huang. Adversarial label learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 3183–3190, 2019.

Diogo Ayres-de Campos, Joao Bernardes, Antonio Garrido, Joaquim Marques-de Sa, and Luis Pereira-Leite. Sisporto 2.0: a program for automated analysis of cardiotocograms. Journal of Maternal-Fetal Medicine, 9(5), 311–318, 2000.

Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alex Ratner, Braden Hancock, and Houman Alborzi. Snorkel DryBell: A case study in deploying weak supervision at industrial scale. Proceedings of the International Conference on Management of Data, 362–375, 2019.

Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. The limitations of deep learning in adversarial settings. IEEE European Symposium on Security and Privacy, 372–387, 2016.

Ivan Homoliak, Miika Teknos, Martin Ochoa, Dominik Breitenbacher, and Saman Hosseini. Improving network intrusion detection classifiers by non-payload-based exploit-independent obfuscations: An adversarial approach. ICST Transactions on Security and Safety, 5, 1–14, 2018.

Richard A. Bridges, George-Vincent Tarrah, Mark D. Iannacone, and Michael S. Vincent. A survey of intrusion detection systems leveraging host data. ACM Computing Surveys, 52(6), 1–35, 2019.

Bo Cao, Chao Li, Yanhui Song, and Xue Fan. Network intrusion detection technology based on convolutional neural network and BiGRU. Computational Intelligence and Neuroscience, 2022, 1–20, 2022.

Bo Dong and Xue Wang. Comparison of deep learning methods to traditional methods for network intrusion detection. Proceedings of the IEEE International Conference on Communication Software and Networks, 581–585, 2016.

Alexandros Rigaki and Ahmed Elragal. Adversarial attacks against supervised machine learning-based network intrusion detection systems. PLOS ONE, 2021.

J. R. Everleigh and E. M. Petrova. A novel adversarial framework for urban traffic congestion analysis: A supply-demand perspective. International Research Journal of Advanced Engineering and Technology, 2(10), 1–11, 2025.

Martin Arjovsky and Léon Bottou. Towards principled methods for training generative adversarial networks. International Conference on Learning Representations, 2017.

Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein GAN. arXiv preprint arXiv:1701.07875, 2017.

Marc G. Bellemare, Ian Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, and Rémi Munos. The Cramér distance as a solution to biased Wasserstein gradients. arXiv preprint arXiv:1705.10743, 2017.

Stephen Booth, Yilun Zhou, Ankit Shah, and Julie Shah. BayesTrEx: A Bayesian sampling approach to model transparency by example. Proceedings of the AAAI Conference on Artificial Intelligence, 2021.

Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale GAN training for high fidelity natural image synthesis. International Conference on Learning Representations, 2019.

Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, and Chris Olah. Activation atlas. Distill, 4, e15, 2019.

Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron Courville. Modulating early visual processing by language. Advances in Neural Information Processing Systems, 6594–6604, 2017.

Most read articles by the same author(s)

<< < 2 3 4 5 6 7 8 > >>

International Research Journal of Advanced Engineering and Technology

Adversarial Learning Under Noise And Weak Supervision: Robust Methodological Foundations And Applications Across Security, Perception, And Socio-Technical Systems

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles