Strengthening our defenses against distillation attacks
Researchers outline new methods to protect machine‑learning models from distillation attacks, where adversaries extract knowledge by training surrogate models on outputs. The proposed defenses include adaptive noise injection, output sanitization, and robust training regimes that limit information leakage. Experiments show reduced success rates for attackers while preserving model accuracy, suggesting practical steps for strengthening AI security.