Google Research

Watermarking the Outputs of Structured Prediction with an application in Statistical Machine Translation

  • Ashish Venugopal
  • Jakob Uszkoreit
  • David Talbot
  • Franz Och
  • Juri Ganitkevitch
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics


We propose a general method to watermark and probabilistically identify the structured outputs of machine learning algorithms. Our method is robust to local editing operations and provides well defined trade-offs between the ability to identify algorithm outputs and the quality of the watermarked output. Unlike previous work in the field, our approach does not rely on controlling the inputs to the algorithm and provides probabilistic guarantees on the ability to identify collections of results from one’s own algorithm. We present an application in statistical machine translation, where machine translated output is watermarked at minimal loss in translation quality and detected with high recall.

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