Robust Machine Learning Models against Adversarial Attacks: A Survey
Abstract
Adversarial attacks pose a significant threat to the reliability and security of machine learning models. This paper presents a comprehensive survey of techniques for defending against adversarial attacks across various domains, including computer vision, natural language processing, and healthcare. We analyze the strengths and limitations of existing defense mechanisms and identify directions for future research in adversarial robustness.
References
McMillan, C., Grechanik, M., Poshyvanyk, D., Fu, C., & Xie, Q. (2011). Exemplar: A Source Code Search Engine with Natural Language Queries. Proceedings of the 2011 International Conference on Software Engineering, 832-835.
Huo, J., Li, G., & Zhou, J. (2021). Learning to Summarize Code by Mining Source Code Summarization Data. Proceedings of the 43rd International Conference on Software Engineering, 1385-1396.
Zhang, J., Xu, H., Zhang, Z., Yang, X., & Wang, H. (2020). Rencos: A Lightweight Embedding for Code Retrieval and Summarization. Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval, 208-211.
Barone, A. V. M., & Sennrich, R. (2017). A Parallel Corpus of Python Functions and Documentation Strings for Automated Code Documentation and Code Generation. Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 314-319.
Fernandes, P., Allamanis, M., & Brockschmidt, M. (2019). Structured Neural Summarization. International Conference on Learning Representations.
Sumit Mittal, "Framework for Optimized Sales and Inventory Control: A Comprehensive Approach for Intelligent Order Management Application," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 61-65, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P109
Hu, X., Li, G., Xia, X., & Lo, D. (2018). Deep Code Comment Generation. Proceedings of the 26th Conference on Program Comprehension, 200-210.
Yao, Y., Zhu, Y., Wang, M., & Lin, H. (2019). Improved Automatic Summarization of Source Code via Deep Learning. Journal of Systems and Software, 156, 328-340.
LeClair, A., McMillan, C., & Treude, C. (2019). Neural Network-based Approaches to Code Summarization: A Survey. arXiv preprint arXiv:2004.01432.
Neha Dhaliwal. (2023). Towards AI-Driven Standardization in Disease Indication: Implementing Controlled Vocabulary for Clinical Reporting Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11), 1142–1151. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10673
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., ... & Shou, L. (2020). CodeBERT: A Pre-Trained Model for Programming and Natural Languages. Findings of the Association for Computational Linguistics: EMNLP 2020, 1536-1547.
Kulbir Singh, "MRI Brain Tumor Segmentation using Cuckoo Optimization and Ensemble CNNs", International Journal of Science and Research (IJSR), Volume 13 Issue 6, June 2024, pp. 425-434, https://www.ijsr.net/getabstract.php?paperid=SR24605090738
Wan, Y., Wang, M., Zhang, Y., Sun, Y., & Xiao, L. (2018). Improving Automatic Source Code Summarization via Deep Reinforcement Learning. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 4159-4165.
Priyanka Koushik, S. M. (2024). Elevating Customer Experiences and Maximizing Profits with Predictable Stockout Prevention Modelling. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1171–1178. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5547
