Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding
This addresses concerns about misuse of language models by providing a defense mechanism for text provenance, though it is an incremental advance in watermarking techniques.
The paper tackles the problem of tracing text provenance by introducing an adversarial watermarking transformer that encodes binary messages into text with minimal semantic changes, achieving effective watermark decoding and robustness against attacks while preserving text utility.
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.