ReDiffMark: Post-Hoc Text Watermarking by Local Re-Diffusion

Post-hoc watermarking embeds detectable signals into existing text, a capability crucial for closed-API outputs, RAG-retrieved passages, and published documents. However, such methods must minimally alter the original text while remaining computationally efficient for large-scale deployment. Existing sparse-editing schemes preserve the text but suffer from poor signal stability and low efficiency due to rule-based byte signaling; conversely, full-rewriting schemes carry strong signals but rewrite most tokens, leading to significant text alteration and slow speeds. We observe that discrete diffusion language models (dLLMs) can bridge this gap. To this end, we propose **ReDiffMark**, a watermarking paradigm based on *localized re-diffusion* for text that is stored or served verbatim or with light edits. We address challenges regarding available contextual entropy and the difficulty of identifying sparse edit locations through entropy-gated editing with key-seeded exponential-minimum (Gumbel) sampling, employing a *replay-based*, surprisal-weighted detector that re-runs the same dLLM on the received text under a calibrated null hypothesis. Extensive experiments across dLLM families, text sources, and passage lengths show that **ReDiffMark** attains high single-passage detection at low edit rates with a constant two-forward-pass cost.