ADS-C: Antidistillation Defense for Classifiers with Zero Utility Cost
A new preprint introduces ADS-C, an antidistillation defense for classification models that provably preserves top-1 accuracy while significantly degrading the performance of student models trained via knowledge distillation. ADS-C adapts gradient-directed perturbation techniques from large language models to classifiers, using a closed-form, per-input margin budget to ensure the teacher model's predictions remain unchanged. The method achieves up to 29.6 percentage point degradation in student accuracy on standard benchmarks, with no loss in teacher accuracy.
Why it matters: ADS-C provides a practical and provable defense against model stealing attacks via knowledge distillation, enabling secure deployment of classifiers without sacrificing their accuracy.
Full story at: arXiv Cryptography and Security ↗