Imitation Attacks and Defenses for Black-box Machine Translation Systems

Understand

Adversaries may look to steal or attack black-box NLP systems, either for financial gain or to exploit model errors.

• One setting of particular interest is machine translation (MT), where models have high commercial value and errors can be costly.
• We investigate possible exploits of black-box MT systems and explore a preliminary defense against such threats.
• We first show that MT systems can be stolen by querying them with monolingual sentences and training models to imitate their outputs.