Attack Performance

Attack performance in machine learning focuses on evaluating and improving the effectiveness of adversarial attacks against various models, aiming to understand and mitigate vulnerabilities. Current research investigates diverse attack strategies, including gradient-based methods, reinforcement learning approaches, and techniques exploiting model-specific weaknesses like power consumption or loss trajectories, often applied to large language models, graph neural networks, and recommender systems. These studies are crucial for enhancing the robustness and security of machine learning systems across numerous applications, from autonomous vehicles to online services, by identifying and addressing critical vulnerabilities. The ultimate goal is to develop more resilient models capable of withstanding a wide range of attacks.