Provable Adversarial Defense

Provable Adversarial Defense is a technique aimed at mathematically ensuring that machine learning models remain robust in the face of adversarial attacks. Its core objective is to provide verifiable security guarantees, enabling models to operate stably in known threat environments. The application value of this technology lies in enhancing the security and credibility of models, particularly in high-risk areas such as finance and healthcare, where it can effectively resist malicious attacks and protect the integrity of data and decisions.