Adversarial Robustness

Adversarial Robustness is the task of evaluating the vulnerability of machine learning models to various adversarial attacks. Its goal is to enhance the model's resistance to malicious inputs, ensuring the stability and reliability of the model in practical applications. Through this task, security vulnerabilities in the model can be identified and repaired, thereby improving the overall security of the system. In adversarial environments, Adversarial Robustness is crucial for protecting data privacy and preventing the model from being manipulated.