[R] PubDef: Defending Against Transfer Attacks Using Public Models

Adversarial attacks pose a serious threat to ML models. But most proposed defenses hurt performance on clean data too much to be practical.

To address this, researchers from UC Berkeley developed a new defense called PubDef. It focuses on defending against a very plausible type of attack - transfer attacks using publicly available surrogate models.

They model the attack/defense game with game theory. This lets PubDef train against diverse attacks simultaneously.

PubDef picks source models covering different training methods - …

adversarial adversarial attacks attacks berkeley clean data data defense machinelearning ml models performance practical public researchers threat transfer type uc berkeley