DenMark: Robust Semantic Watermarking for Diffusion Language Models

Semantic text watermarks encode signals in meaning rather than surface token choices, making them inherently more robust to paraphrasing and other semantic-preserving edits. Existing semantic watermarking methods, however, are primarily designed for autoregressive language models (ARLMs), where candidate semantic units can be completed and evaluated before generation proceeds. This assumption breaks down for diffusion language models (DLMs), whose intermediate states contain incomplete semantic units and whose tokens can be updated in flexible orders. We propose DenMark, a semantic watermarking framework that injects key-dependent signals directly into the DLM denoising process. DenMark partitions the output into fixed token regions and introduces temporary rollouts as semantic lookahead: conditional completions are sampled from an incomplete region to estimate its eventual semantic watermark score, allowing DenMark to favor local denoising updates that are expected to strengthen the watermark. Repeating this procedure throughout generation progressively accumulates semantic watermark evidence in the final output. For detection, DenMark performs calibrated scanning over candidate unit sizes, improving robustness to boundary shifts caused by semantic-preserving edits. Across four DLM backbones, three datasets, and four semantic attacks, DenMark consistently outperforms existing watermarking baselines, achieving the highest TPR in 46 of 48 backbone–dataset–attack settings at the reported operating points. These results demonstrate that semantic watermarking can be effectively integrated into the flexible, non-autoregressive generation process of DLMs while retaining strong robustness to semantic edits.