Your Non-Transferable Learning is Fragile: Practical Breach of Protected Models

Researchers developed a new attack called Distribution Drift Learner (DDL) that can break through non-transferable learning (NTL, a method that prevents AI models from being adapted to new tasks to protect their intellectual property) by only observing the model's input and output responses. The attack works by manipulating how data is distributed across domains and reconstructing training samples, successfully increasing accuracy on protected models from 10% to 81%, exposing serious weaknesses in current model protection strategies.