Adversarial Perspective

Adversarial perspective research investigates the vulnerabilities of various machine learning models, including large language models, graph neural networks, and deep reinforcement learning agents, to malicious attacks or unexpected inputs. Current research focuses on developing and evaluating adversarial attacks against these models, often employing techniques like adversarial training and exploring the transferability of attacks across different architectures. This work is crucial for enhancing the robustness and reliability of AI systems across diverse applications, ranging from improving the safety of autonomous systems to ensuring the fairness and privacy of data-driven decision-making.