Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding

TL;DR

This paper presents the Adversarial Watermarking Transformer, an end-to-end model that encodes watermarks into text to trace provenance while maintaining semantic integrity and resisting attacks.

Contribution

It introduces the first automatic, end-to-end watermarking model for text that learns to hide data without ground truth, enhancing text provenance tracing.

Findings

Effective in preserving text utility and semantics

Successfully decodes watermarks with high accuracy

Robust against various attack strategies

Abstract

Recent advances in natural language generation have introduced powerful language models with high-quality output text. However, this raises concerns about the potential misuse of such models for malicious purposes. In this paper, we study natural language watermarking as a defense to help better mark and trace the provenance of text. We introduce the Adversarial Watermarking Transformer (AWT) with a jointly trained encoder-decoder and adversarial training that, given an input text and a binary message, generates an output text that is unobtrusively encoded with the given message. We further study different training and inference strategies to achieve minimal changes to the semantics and correctness of the input text. AWT is the first end-to-end model to hide data in text by automatically learning -- without ground truth -- word substitutions along with their locations in order to encode the message. We empirically show that our model is effective in largely preserving text utility and decoding the watermark while hiding its presence against adversaries. Additionally, we demonstrate that our method is robust against a range of attacks.