Adversarial Risk

Adversarial risk focuses on the vulnerability of machine learning models to malicious inputs designed to cause misclassification or other undesirable outputs. Current research investigates various attack methods, including test-time data poisoning and attacks targeting specific model components (e.g., face detectors, language models), and explores defense strategies such as adversarial training, robust model architectures (e.g., Vision Transformers), and novel loss functions. Understanding and mitigating adversarial risk is crucial for deploying reliable machine learning systems in security-sensitive applications, driving ongoing efforts to develop more robust models and rigorous evaluation methods.