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Text-Preserving Watermarking Framework

Updated 14 October 2025
  • Text-preserving watermarking frameworks are systems designed to embed hidden, statistically detectable signals into digital text without altering its semantic or visual quality.
  • They employ diverse techniques—such as image-based, token-level, and semantic-level methods—to ensure robustness against common attacks like paraphrasing and compression.
  • The frameworks balance imperceptibility, efficacy, and resilience by leveraging statistical testing, cryptographic keys, and optimal transport for reliable detection.

A text-preserving watermarking framework is a system designed to embed robust, statistically detectable signals into written digital content—such as natural language text or text documents—without significantly altering semantic fidelity, visual features, or distributional properties. These frameworks are deployed for copyright protection, provenance tracking, authentication, fingerprinting, and resistance to unauthorized distribution. The defining challenge is to strike a balance among imperceptibility, efficacy, robustness against attacks, and generality across languages and modalities.

1. Fundamental Principles and Definitions

Text-preserving watermarking frameworks operate on the principle of embedding a hidden but verifiable signal within a text artifact such that:

  • The embedded watermark survives surface-level modifications (e.g., print-scan cycles, synonym substitutions, paraphrasing, compression, or format changes) but ideally disappears or degrades under semantic-altering or adversarial attacks.
  • The visual, syntactic, or semantic quality of the text is preserved, i.e., the output distribution remains as close as possible to the original unwatermarked text.
  • The presence, absence, or payload of the watermark can be reliably detected using statistical hypothesis testing, cryptographic key knowledge, or semantic retrieval.

Frameworks vary in their methods for defining the embedding domain—including text rendering as images, working at the lexical level, or operating on semantic, n-gram, or document-level representations—and in their robustness–imperceptibility trade-offs.

2. Key Architectural Approaches

Text-preserving watermarking encompasses several major architectural paradigms, including but not limited to:

Paradigm Description Representative Example
Image-based Treats text as image blocks and employs image WM mPDF (Mehta et al., 2016), CoreMark (Meng et al., 29 Jun 2025)
Token-level (LLM) Alters token probabilities, selection, or logits Unigram-Watermark (Zhao et al., 2023), WaterPool (Huang et al., 22 May 2024), CATMark (Zhang et al., 27 Sep 2025)
Semantic-level Embeds watermarks at the sentence/meaning level PMark (Huo et al., 25 Sep 2025)
Post-hoc Modifies output by rewriting or synonym substitution Black-box method (Yang et al., 2023)
Cross-modal Coordinates text WM with images or multimodal data VLA-Mark (Liu et al., 18 Jul 2025)

Image-based techniques leverage energy and texture analysis, processing documents as blockwise images and embedding watermark signals in texture-rich regions only. Token-level schemes, widely used for LLMs, embed watermarks by modulation of token selection probabilities according to a secret key and auxiliary random functions. Semantic–level frameworks utilize sentence representations and projection operators (“proxy functions”) to select or partition generated sentences in a way that leaves the statistical text distribution unchanged on average. Post-hoc methods apply controlled synonym substitutions or paraphrasing operations on generated text, relevant in black-box or API-limited settings.

3. Methodological Components and Embedding Strategies

Embedding a watermark into text while preserving its core characteristics typically involves several specialized methodologies:

  • Block segmentation and texture analysis: For image-based frameworks, such as mPDF (Mehta et al., 2016), text pages are cropped and split into fixed-size blocks, classified as “texture” or “non-texture” using DCT energy measures. Only texture blocks are selected for embedding.
  • Content-adaptive embedding strength: The perturbation magnitude (α) is tuned according to local block or context energy (e.g., lower for pure text, higher for mixed text/graphics).
  • Key and mark module decomposition: As in WaterPool (Huang et al., 22 May 2024), modern frameworks separate the key module (responsible for random sampling and imperceptibility guarantees) from the mark module (responsible for the distributional modification). WaterPool employs semantic retrieval to restore keys, enhancing robustness without reducing keyspace entropy.
  • Statistical modulation of probabilities or distributions: Techniques such as Unigram-Watermark (Zhao et al., 2023) and CATMark (Zhang et al., 27 Sep 2025) boost the logits of a secret “green list” of tokens; the boost is conditioned on the output entropy or a dynamically clustered semantic state, so as not to affect deterministically generated content like code.
  • Proxy functions and multi-channel constraints: PMark (Huo et al., 25 Sep 2025) defines sentence-level “channels” via scalar projection functions in a semantic embedding space, dynamically estimating medians to split candidate sentences. Multiple channels amplify watermark evidence while preserving output distribution (“distortion-free” property).
  • Optimal transport and code design: HeavyWater and SimplexWater (Tsur et al., 6 Jun 2025) formalize watermarking as an optimization over score functions and token coupling distributions using Sinkhorn’s algorithm. SimplexWater leverages coding theory for maximal Hamming separation between tokens.

4. Robustness, Imperceptibility, and Detection

Robustness and imperceptibility are central criteria for framework evaluation:

  • Objective metrics: Across frameworks, quality metrics such as PSNR > 45 dB, SSIM > 0.99 (for image-based), and perplexity/statistical detection power (AUC, TPR at FPR, z-score separation, ROC-AUC) are reported as evidence for insufficient perceptual alteration and strong watermark detectability (mPDF (Mehta et al., 2016), CoreMark (Meng et al., 29 Jun 2025), Waterfall (Lau et al., 5 Jul 2024), PMark (Huo et al., 25 Sep 2025)).
  • Resistance to common attacks: Techniques are evaluated under print-scan cycles, paraphrasing, synonym substitution, re-translation, print screen, stitching, rotational or compression distortions. Semantic-aware and hybrid methods such as SynGuard (Han et al., 27 Aug 2025) and contrastive-learning approaches (An et al., 9 Apr 2025) provide resilience even under sophisticated paraphrase or “spoofing” attacks.
  • Adaptively selective embedding: CATMark (Zhang et al., 27 Sep 2025) and entropy-sensitive mechanisms (Liu et al., 18 Jul 2025) prevent embedding in low-entropy (predictable) contexts, avoiding harm to functional content (such as code) and preserving the expected output distribution—a property called “unbiasedness” (Wu et al., 28 Sep 2025).

Detection is typically achieved via a statistical hypothesis test: for example, counting green token occurrences (z-score in (Zhao et al., 2023)), aggregation of evidence across channels or keys (ensemble signal amplification in (Wu et al., 28 Sep 2025)), or by measuring the inner product with the embedded watermark in a permuted logit space (Waterfall (Lau et al., 5 Jul 2024)). Semantic-level methods may employ retrieval or projection comparisons over sentence embeddings.

5. Trade-offs, Theoretical Guarantees, and Innovations

Modern frameworks explicitly address and often attempt to resolve inherent trade-offs:

  • Imperceptibility vs. efficacy vs. robustness: WaterPool (Huang et al., 22 May 2024) isolates the key module’s contribution to these trade-offs and demonstrates that semantic-based key restoration can mitigate the traditional conflict between large keyspace (imperceptibility) and efficient detection (efficacy).
  • Unbiasedness and ensemble methods: Ensemble frameworks (Wu et al., 28 Sep 2025) compose multiple unbiased watermark layers, provably preserving the expected output distribution while enhancing signal-to-noise for detection, up to an optimal ensemble size limited by promotion sparsity and token set shrinkage.
  • Coding-theoretic optimality: The maximization of detection gaps in low-entropy regimes is linked directly to classical coding theory (e.g., Simplex code achieves the Plotkin bound (Tsur et al., 6 Jun 2025)).
  • Distortion-free property and robust semantics: PMark’s proxy-function approach (Huo et al., 25 Sep 2025) provides formal guarantees that the geometric distribution of watermarked sentences matches the original generator, with channel constraints boosting resilience to paraphrasing without detectably shifting the LM output.

6. Applications, Generality, and Future Directions

Applications span PDF copyrighting, LLM provenance, API-based watermarking, article and code watermarking, and even multimodal systems:

  • Language and font generality: Frameworks such as CoreMark (Meng et al., 29 Jun 2025) and mPDF (Mehta et al., 2016) demonstrate effectiveness across numerous languages, font styles, and document structures.
  • Data provenance and forensic attribution: Waterfall (Lau et al., 5 Jul 2024) and PMark (Huo et al., 25 Sep 2025) are explicitly motivated by the need to trace LLM-generated or LLM-trained content, both for legal enforcement and for scientific reproducibility.
  • Multimodal content protection: VLA-Mark (Liu et al., 18 Jul 2025) targets the preservation of semantic-critical alignment in image–text pairs, establishing benchmarks for multimodal watermarking that do not degrade vision-language coherence.
  • Industrial and practical deployment: Efficient detection algorithms, open-source releases, and empirically validated performance under attacks and translation position these frameworks for real-world deployment, with ongoing research focused on further reducing computational overhead, enhancing key management, and developing frameworks that accommodate even more sophisticated adversarial strategies.

A plausible implication is that the increasing integration of semantic, statistical, and cryptographic components—in conjunction with adaptive context analysis—will continue to drive improvements in robustness, efficacy, and imperceptibility for text-preserving watermarking frameworks across modalities and applications.

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