Certified Defense

Certified defense in machine learning aims to create models provably robust against adversarial attacks, offering theoretical guarantees of accuracy even when inputs are maliciously perturbed. Current research focuses on improving the generalizability of these defenses across different data distributions and attack types, employing techniques like causal inference, confidence-based filtering, and diffusion models to enhance robustness. This field is crucial for deploying trustworthy machine learning systems in security-sensitive applications, as certified defenses provide a higher level of assurance compared to purely empirical approaches.