Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks

Understand

High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applications e.g., cloud prediction APIs.

• Recent advances in model functionality stealing attacks via black-box access (i.e., inputs in, predictions out) threaten the business model of such applications, which require a lot of time, money, and effort to develop.
• Existing defenses take a passive role against stealing attacks, such as by truncating predicted information.
• We find such passive defenses ineffective against DNN stealing attacks.