Nonintrusive Watermarking as a Service

• NWaaS is a service-driven watermarking paradigm that introduces, manages, and verifies ownership evidence via side channels while leaving the underlying model untouched.
• Multiple mechanisms, from inference-time output mediation to data-side augmentation, are employed to counteract model theft and adversarial extraction attacks.
• Research in NWaaS spans diverse deployment settings, balancing robust verification with minimal disruption to normal model performance.

Searching arXiv for the cited NWaaS-related papers and closely related watermarking work. Nonintrusive Watermarking as a Service (NWaaS) is a service-oriented watermarking paradigm in which ownership evidence is introduced, managed, and verified with minimal disruption to the protected artifact’s normal use. In its explicit formulation for X-to-Image systems, the protected model remains entirely untouched and an owner-defined watermark is extracted from model outputs through a side channel around the black-box API; closely related work applies the same service logic to post-deployment MLaaS protection, embedding APIs, multimodal RAG, and white-box verification workflows (An et al., 24 Jul 2025, Xu et al., 16 Jan 2025).

1. Conceptual scope and meanings of “nonintrusive”

The literature does not use “nonintrusive” in only one sense. In the strictest formulation, NWaaS requires that the protected model be frozen and unchanged: no parameter modification, no architecture modification, no fine-tuning, and no change to the model’s normal inference outputs. This is the position taken by ShadowMark’s NWaaS formulation for X-to-Image models, where “absolute fidelity” follows from leaving the original model path untouched (An et al., 24 Jul 2025). A broader service interpretation appears in post-deployment MLaaS protection, where watermarking is called “plug-and-play” and “training-free” because it is shifted from parameter space into prediction space after deployment, even though inference-time integration with logits and hidden features is still required (Xu et al., 16 Jan 2025).

A second meaning of nonintrusiveness concerns the service boundary rather than the model boundary. In FreeMark, the host DNN is not modified at all; instead, secret verification keys are derived from activation statistics and stored with a trusted third party, so watermarking becomes a registration-and-verification procedure rather than an embedding procedure in the usual sense (Chen et al., 2024). In embedding services, nonintrusiveness is often described as harmlessness and stealthiness: the provider alters returned embeddings, but does so in a way intended to preserve utility and avoid conspicuous behavior in downstream tasks (Li et al., 18 Dec 2025).

A third meaning is infrastructural. Earlier cloud-oriented watermarking work already shifted responsibility from users to the cloud provider, and encrypted-domain watermarking for numerical data explicitly nominated the cloud as a neutral judge that watermarks and verifies without plaintext exposure (Cusack et al., 2016, Alqarni, 2023). This suggests that NWaaS is best treated as a family of service patterns rather than a single algorithmic template.

2. Threat models and deployment loci

NWaaS is motivated by service-side theft models in which the attacker does not need weights, source code, or training data. In the MLaaS setting studied by Neural Honeytrace, a malicious user collects a surrogate dataset $\mathbb{D}_s=\{X_1,\dots,X_n\}$, queries the deployed API for outputs $\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$, and trains a surrogate $\mathcal{F}_s$ by minimizing

$$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$

This is precisely the operational context in which a post-deployment watermarking service must work (Xu et al., 16 Jan 2025).

Embedding services instantiate the same pattern with vector outputs rather than class probabilities. WET treats the watermark as a post-hoc transformation of embeddings returned by an API, motivated by imitation attacks on Embeddings-as-a-Service; RegionMarker and SemMark instead assume a provider that intercepts outgoing embeddings and injects watermark structure conditioned on secret semantic regions (Shetty et al., 2024, Yang et al., 17 Nov 2025, Li et al., 18 Dec 2025). Multimodal RAG introduces a data-side analogue: AQUA does not modify retrievers or generators, but augments a contributed image knowledge base so that watermark signals propagate indirectly from retrieved image semantics into generated text under black-box API access (Chen et al., 10 Jun 2025).

Across these systems, the service insertion point recurs in four places. First, there is inference-time output mediation, as in Neural Honeytrace and embedding-API watermarking. Second, there is an API-side auxiliary channel around a frozen model, as in ShadowMark. Third, there is data-side augmentation of contributed knowledge, as in AQUA. Fourth, there is external key escrow and white-box verification, as in FreeMark and other TTP-centered schemes (An et al., 24 Jul 2025, Chen et al., 2024). A plausible implication is that NWaaS is less about a single watermark representation than about where watermark control is placed relative to the model, the serving stack, and later forensic access.

The attack literature around these service loci is correspondingly broad. Evaluations now include not only naive extraction but adaptive attacks such as CSE for embedding watermarks, paraphrase averaging, smoothing, label recovery, hard-label-only extraction, Detect-Sampling against conspicuous verification traffic, dimensionality reduction, and oracle settings in which the attacker learns from defense-modified outputs (Shetty et al., 2024, Shetty et al., 2024, Xu et al., 16 Jan 2025, Li et al., 18 Dec 2025).

3. Technical mechanisms

Neural Honeytrace’s main conceptual move is to treat watermark survival under model extraction as a communication problem. It defines a watermark message $W$, an encoding rule $\hat O = O \oplus f_n(W,\mathbb{A})$, and a channel whose capacity follows Shannon-style reasoning, with the paper taking $X=Y=O$ so that $C=H(O)$. The key claim is that what propagates through extraction is not an on/off trigger event but the similarity between attacker queries and watermark triggers. The resulting system computes a similarity score in the last latent space,

$$s = d-\frac{1}{N}\sum_{i=1}^N \big[f_{-1}(X)-f_{-1}(W_i)\big]^2,$$

then mixes logits by

$$l_{\text{mix}}=(1-s^\alpha)\,l_{\text{ori}}+s^\alpha\,l_{\text{ref}},$$

and finally applies a similarity-based probabilistic label-flipping step so that watermark information is transmitted through label distributions over many extraction queries rather than isolated triggers (Xu et al., 16 Jan 2025).

Embedding services exhibit a different family of mechanisms. WET applies a secret linear transform to every returned embedding,

$\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$0

and later reverses it with the pseudoinverse,

$\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$1

Its central theoretical claim is that paraphrase averaging preserves the secret transform $\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$2, which is why WET is reported to be robust against paraphrasing even when prior trigger-word schemes are not (Shetty et al., 2024). RegionMarker replaces lexical triggers with secret semantic regions: it projects an embedding by $\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$3, partitions the reduced space with locality-sensitive hashing, samples a subset of regions as triggers, and if a text falls into trigger region $\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$4 returns

$\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$5

Its robustness claim is that watermarking an entire semantic region, rather than a few trigger words, makes removal, paraphrasing, and dimension perturbation harder to evade (Yang et al., 17 Nov 2025). SemMark generalizes the semantic approach by learning a watermark mapping $\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$6 such that $\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$7, then using a local-outlier-factor-based adaptive weight $\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$8 to inject

$\mathbb{Y}_s=\{Y_1,\dots,Y_n\}$9

with watermarking restricted to LSH-defined semantic buckets. The adaptive weighting is explicitly introduced to preserve the original embedding distribution (Li et al., 18 Dec 2025).

ShadowMark implements the strictest NWaaS conception. The watermark is not embedded in parameters at all. A key encoder $\mathcal{F}_s$0 transforms a secret key into a crafted query, the untouched protected model $\mathcal{F}_s$1 processes that query, and a watermark decoder $\mathcal{F}_s$2 maps the output back to the owner-defined watermark. Training optimizes a correct-key loss,

$\mathcal{F}_s$3

together with wrong-key and normal-query repulsion terms, while $\mathcal{F}_s$4 remains frozen (An et al., 24 Jul 2025).

AQUA moves watermarking entirely to the knowledge base. It inserts synthetic images into a multimodal RAG corpus and relies on indirect propagation: a trigger query retrieves the watermarked image, and the generator converts acronym cues or spatial relations into a textual signature. The formal retrieval and generation path is

$\mathcal{F}_s$5

while verification is based on exact-match detection of a predefined signature in the generated answer (Chen et al., 10 Jun 2025).

4. Verification, evidence, and service orchestration

Verification procedures in NWaaS vary from black-box statistical testing to white-box key-escrow extraction. Neural Honeytrace uses watermark success rate (WSR) and a sample-size formula

$\mathcal{F}_s$6

with $\mathcal{F}_s$7 taken as effect size. The paper’s abstract reports that the average number of samples required for a worst-case t-Test-based copyright claim is reduced from $\mathcal{F}_s$8 to $\mathcal{F}_s$9 with zero training cost, which turns watermark verification into a much more practical forensic operation under black-box access (Xu et al., 16 Jan 2025).

SemMark and RegionMarker verify statistically by splitting benign and watermark-region texts, then comparing cosine- and $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$0-based alignment scores and performing a Kolmogorov–Smirnov test between the two distributions. SemMark defines

$$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$1

with an analogous $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$2, and treats $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$3 as successful verification; RegionMarker uses the same style of KS-test plus $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$4 and $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$5 aggregation across watermark regions (Li et al., 18 Dec 2025, Yang et al., 17 Nov 2025). AQUA uses a different evidentiary stack: exact-match evaluation $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$6, aggregate metrics such as VSR and CGSR, and Welch’s t-test against clean and watermarked reference distributions. In its reported setup, $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$7 and $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$8 reach significance within 30 queries, whereas the optimization-based baseline requires more than 200 (Chen et al., 10 Jun 2025).

ShadowMark verifies original protected models with $$\arg\min_{\mathcal{F}_s}\; \mathbb{E}_{(X,Y)\in (\mathbb{D}_s,\mathbb{Y}_s)}\big[\mathcal{L}(\mathcal{F}_s(X),Y)\big].$$9 and stolen surrogates with normalized cross-correlation difference,

$W$0

declaring success when $W$1. This shifts verification from simple watermark extraction toward theft detection under model imitation (An et al., 24 Jul 2025).

White-box systems follow different evidentiary models. ClearMark extracts a visible image-like watermark through a transposed model and explicitly argues that ownership should ideally be assessed by human inspection rather than rigid scalar thresholds, although SSIM, MSE, and BER are used as supporting signals during training and evaluation (Krauß et al., 2023). FreeMark formalizes verification as BER between a registered watermark vector $W$2 and an extracted vector $W$3,

$W$4

with the TTP holding the secret keys $W$5 that are needed to extract $W$6 from suspect-model activations (Chen et al., 2024).

For practical service operation, one recurring recommendation is to treat watermark assets as durable evidence. Neural Honeytrace’s deployment discussion is explicit on this point: an implementation should maintain the watermark pool and subset, feature embeddings, composite-trigger masks, target classes and reference logits, hyperparameters, timestamped deployment logs, verification query sets, expected WSR ranges, and audit logs of ownership-claim runs (Xu et al., 16 Jan 2025).

5. Domain-specific embodiments

NWaaS is not confined to one model class. In classifier-serving environments, Neural Honeytrace is a post-deployment protection layer for MLaaS APIs. In embedding services, the line from WARDEN through WET to RegionMarker and SemMark shows a progression from trigger-word backdoors, to global linear transformations, to semantic-region and semantic-mapping schemes that aim to resist CSE, paraphrasing, Detect-Sampling, and dimension-perturbation attacks while preserving embedding utility (Shetty et al., 2024, Shetty et al., 2024, Yang et al., 17 Nov 2025, Li et al., 18 Dec 2025). In multimodal RAG, AQUA treats watermarking as image-knowledge provenance rather than model watermarking, yet it fits the same service pattern because it requires only data-side insertion and black-box audit (Chen et al., 10 Jun 2025).

The service concept also extends to backbone-level or white-box ownership systems. TokenMark is modality-agnostic across pre-trained Transformers because it exploits permutation equivariance to hide watermark behavior behind a secret feature permutation and a small decoder, yielding a trigger-sample-free verification mechanism that can be used with black-box access once the watermarked Transformer has been fine-tuned (Xu et al., 2024). ClearMark is a basis for a white-box, explainable NWaaS offering because it embeds visible secrets into all model parameters through transposed model training and later extracts an image-like watermark that can be judged directly by humans (Krauß et al., 2023). FreeMark goes further toward service externalization by moving the watermark almost entirely into activation-derived keys stored with a TTP, leaving the host model unchanged (Chen et al., 2024).

Image-side watermark engines are also relevant even when they are not framed as NWaaS. TrustMark is an imperceptible GAN-based image watermarking system with arbitrary-resolution support and a dedicated remover for re-watermarking; its reported role in provenance labeling, copyright tagging, and responsible generative-AI image labeling makes it a plausible engine inside an image-focused service layer (Bui et al., 2023). Earlier systems such as ISWAR and cloud-rightful-ownership architectures likewise show that service-oriented watermarking long predates the term NWaaS, even if their verification assumptions are more cumbersome and their deployment model is less cloud-native (Mohanty, 2012, Cusack et al., 2016).

6. Limitations, tensions, and research directions

The strongest limitation is that “nonintrusive” often hides a substantial integration burden. Neural Honeytrace is training-free but not a pure black-box retrofit, because the mechanism assumes access to logits and last-layer features; ClearMark requires transposed-model construction and retraining or fine-tuning; FreeMark requires white-box access to internal activations at verification time (Xu et al., 16 Jan 2025, Krauß et al., 2023, Chen et al., 2024). Thus, nonintrusive usually means nonintrusive to training or user-facing inference, not necessarily nonintrusive to the serving stack or forensic workflow.

A second tension concerns trust. ShadowMark explicitly aims for a trustless system in which key management and verification are integrated into the cloud infrastructure without reliance on a trusted authority, whereas FreeMark and encrypted-domain watermarking for numerical data place a trusted third party or neutral cloud judge at the center of verification (An et al., 24 Jul 2025, Chen et al., 2024, Alqarni, 2023). These are materially different service philosophies: one minimizes trust in external escrow, the other institutionalizes it.

A third issue is evaluation realism. The literature repeatedly shows that robustness claims drawn from naive attack models can be overstated. WARDEN’s CSE attack reconstructed EmbMarker’s single target embedding at extremely high cosine similarity and removed the watermark while maintaining embedding utility; WET showed that lexical-trigger EaaS watermarks are vulnerable to paraphrase averaging; Neural Honeytrace’s threat model highlights smoothing, label recovery, Top-1 extraction, and oracle attacks; SemMark’s Detect-Sampling results show that verification traffic itself can become an attack surface if it is conspicuous (Shetty et al., 2024, Shetty et al., 2024, Xu et al., 16 Jan 2025, Li et al., 18 Dec 2025). A plausible implication is that NWaaS should be benchmarked under adaptive extraction, recovery, traffic filtering, compression, and deployment-specific API constraints, not only under generic removal attacks.

The broadest unresolved tension is dual use. The monitoring paper argues that watermarking should be treated as a monitoring primitive: internal monitoring is unavoidable under per-entity keys and detector access, and external monitoring can emerge over time from persistent key-dependent statistical structure (Aremu et al., 13 May 2026). For NWaaS, this means that per-user or per-tenant attribution keys are simultaneously ownership tools and observability tools. Distribution-preserving or undetectable schemes, shared or group keys, restricted detector access, and evaluation against observer-based aggregation are therefore not peripheral concerns; they are central to any serious claim that a watermarking service is nonintrusive.

In current research, the most plausible trajectory is a split architecture. One branch pursues strict non-modification of deployed models through side channels, output shaping, or external keying; another pursues stronger evidentiary and explainability properties through white-box extraction, visible secrets, or TTP-backed verification. NWaaS, in that sense, is becoming less a single algorithmic object than a service layer spanning model protection, provenance, audit, and governance.