Adaptive Code Watermarking Framework

• Adaptive code watermarking frameworks are systems that use context-aware algorithms to embed invisible watermarks in source code, preserving functionality and ensuring intellectual property protection.
• They employ techniques such as variable renaming, token biasing, and semantic-preserving transformations to optimize watermark stealth, robustness, and detectability.
• These frameworks enable reliable code provenance, ownership attribution, and defense against adversarial attacks, making them essential for modern code generation and verification.

An adaptive code watermarking framework refers to a system or architecture for embedding, detecting, and verifying watermarks in source code or code artifacts, where the watermarking strategy is able to respond to code structure, usage context, or adversarial threats. The goal is to provide intellectual property protection, code provenance, and traceability for code generated or processed by modern tools such as LLMs or neural code completion systems. Adaptive frameworks distinguish themselves by employing data- or context-driven algorithms that optimize watermark invisibility, functional preservation, and robustness under a broad range of attacks and transformations.

1. Key Architectural Principles

State-of-the-art adaptive code watermarking frameworks typically employ the following principles:

• Context-Aware Watermarking: Watermark insertion is guided by structural, syntactic, or semantic characteristics of code, such as variable contexts, non-critical token identification, or AST analysis, ensuring that functional behavior is maintained (Li et al., 2023, Kim et al., 26 Feb 2025).
• Content- or Semantics-Preserving Transformations: Transformations such as variable renaming, reordering of commutative operations, rewrites of equivalent expressions, or API substitutions maintain semantic equivalence, as in adaptive semantic-preserving transformation-based approaches (Sun et al., 2023, Li et al., 12 Feb 2024).
• Iterative or Policy-Driven Optimization: Reinforcement learning or iterative refinement allows the watermarking policy to learn which tokens or code regions are suitable for watermarking, balancing watermark detectability and code correctness (Guo et al., 16 Aug 2025, Wu et al., 19 May 2025).
• Adversarial Robustness and Dynamic Adaptation: The framework can adjust strategies (embedding sites, error correction rates, etc.) to counteract adversarial modifications, code refactoring, or model extraction attacks (Lei et al., 18 Nov 2024, Xu et al., 16 Jan 2025).
• Black-box and Training-Free Operation: In some frameworks, watermarking is performed post hoc, without retraining or fine-tuning models, allowing plug-and-play or “on the fly” deployment (Li et al., 12 Feb 2024, Xu et al., 16 Jan 2025, Lau et al., 5 Jul 2024).

2. Watermark Embedding Algorithms and Techniques

Variable and Identifier Modification

• Graph neural network-based context modeling selects variable substitutions that carry information, leveraging variable context graphs to ensure that watermarkings are both robust and maintainable (Li et al., 2023). Embedding bits is achieved through the careful transformation of variable names, guided by context and knowledge distillation from pre-trained models such as CodeBERT.
• The embedding is made highly flexible: the same variable can carry distinct watermark bits in different contexts due to local graph representations.

Token Biasing and Soft Watermarks

• Adaptive token-level watermarking (as in STONE or CodeTracer) uses RL or heuristic policies to select non-syntax tokens for watermark embedding. During generation, tokens belonging to a “green list”—a dynamically chosen subset of the vocabulary—are assigned a higher sampling probability by adding a bias term to their logits:

$$\tilde{l}_j = l_j + w \cdot \delta \cdot \mathbb{1}_{j \in G}$$

with $w$ as a binary watermark flag and $G$ as the adaptive green list (Guo et al., 16 Aug 2025, Kim et al., 26 Feb 2025).

Semantic-Preserving Line-Level Transformations

• Frameworks such as CodeMark deploy a library of semantic-preserving transformation rules (e.g., expansion of syntactic sugar, default parameter rewriting), chosen adaptively and applied to maximize the stealth and robustness of the watermark (Sun et al., 2023, Li et al., 12 Feb 2024). Each transformation can be made idempotent, facilitating efficient encoding and detection.

Multi-Bit and Context-Sensitive Embedding

• Multi-bit watermarks (i.e., embedding more than a single tag) are supported by grammar-guided approaches such as CodeIP, where a hash function applied to the message, context, and candidate token deterministically selects which tokens receive watermark logit bias (Guan et al., 24 Apr 2024). Grammar and lexical type predictors further constrain the candidate set to preserve code well-formedness.

3. Verification, Detection, and Robustness

Reliable Detection Mechanisms

• Verification modules often use statistical tests (e.g., one-proportion z-test on watermarked token distribution, t-tests for trigger–target pair co-occurrences in datasets (Sun et al., 2023)) or cryptographic proof (zero-knowledge proof using embeddings (Zhang et al., 4 Feb 2025)) to assert watermark presence without revealing the underlying signature.
• Adaptive frameworks may employ robust key-set selection (e.g., choosing instances that maximize difference in confidence between watermarked and non-watermarked models (Shams et al., 2022)) and verification models (logistic regression or Naive Bayes classifiers) for black-box model watermark detection.

Error Correction and Idempotence

• Error correction codes, such as BCH, are used to encode watermark messages and tolerate bit-level extraction errors, allowing exact recovery even after limited code modifications (Li et al., 12 Feb 2024).

Evaluation Metrics

• CWEM (Code Watermarking Evaluation Metric) is introduced to jointly measure Correctness, Detectability, and Naturalness, rewarding frameworks that inject robust and stealthy watermarks without sacrificing functionality (Kim et al., 26 Feb 2025).
• Metrics reported include detection accuracy (AUROC), pass@k for functional correctness, bitwise extraction accuracy, and perplexity-based naturalness.

4. Security, Attack Resilience, and Adaptive Defenses

• Resistance to model extraction and adaptive attacks is a core driver. Neural Honeytrace models the watermark as a message in the output distribution, using plug-and-play similarity-based logit mixing and distribution-based multi-step information transmission to thwart smoothing or recovery-based removal (Xu et al., 16 Jan 2025).
• Concept-oriented frameworks like Conceptwm (Lei et al., 18 Nov 2024) embed watermarks in the model's latent concept space and utilize adversarial optimization to ensure that fine-tuning (e.g., by DreamBooth) cannot easily purge the watermark without substantial utility loss.
• AEB-robustness (Cohen et al., 17 May 2024) formalizes the requirement that even partial outputs (as in adaptive prompting) must maintain watermark detectability if “enough” blocks of the generation are present, enabling multi-user tracing and strong robustness guarantees.

5. Comparative Analysis and Performance

Framework	Embedding Method	Adaptivity Source	Watermark Capacity	Evaluation Metrics	Robustness/Resilience
CodeTracer	RL token biasing	Policy/context	Multi-bit	AUROC, Pass@1, z-score	Survives paraphrasing, renaming (Guo et al., 16 Aug 2025)
STONE	Non-syntax token bias	Syntax token exclusion	Single-bit	CWEM, AUROC, perplexity	High correctness, detectability (Kim et al., 26 Feb 2025)
CodeMark	SPT and variable renaming	Context/context graph	Variable (by SPT)	BLEU, EM, t-test, human	Imperceptible, robust to dilution (Sun et al., 2023)
RoSeMary	ML/Crypto codesign	Joint end-to-end/CodeT5	Multi-bit	AUROC, functional loss	Secure ZKP ownership, adversarial
ACW	Idempotent transforms	AST-based applicability	Configurable (BCH)	BitACC, TPR, FPR	Black-box, no retraining, robust (Li et al., 12 Feb 2024)
DeCoMa	Abstraction(outlier det.)	Dual channel detection	Detect/removal	Recall, run-time	Defeats stealth triggers (Xiao et al., 9 Apr 2025)

Differences arise in how frameworks balance stealth (imperceptibility), attack resilience, watermark capacity, efficiency, and integration with developer tooling. Error correction, parallel or adversarially trained insertion, and detection via abstraction statistics extend robustness against code modifications and adaptive threats.

6. Applications and Implications for Code Provenance

• Ownership Attribution: Embedding watermarks as adaptive, robust signals supports code provenance tracking—even as generated code is paraphrased, modified, or combined—enabling legal IP protection, academic integrity enforcement, or forensic audits (Sun et al., 2023, Li et al., 12 Feb 2024).
• Malware and Vulnerability Tracking: In scenarios where certain AI agents generate potentially risky code, adaptive watermarking allows the identification of both the authoring model and the responsible party (Li et al., 12 Feb 2024).
• Mitigating Model Extraction: Plug-and-play embedding (as in Neural Honeytrace) prevents exfiltrated models from evading watermark-based detection, providing a scalable defense for MLaaS deployments (Xu et al., 16 Jan 2025).

7. Open Problems and Future Directions

• Extreme-Scale and Multi-Language Adaptation: Extending the frameworks to support more programming languages and character sets, as well as scaling to millions of users and tokens, as in Waterfall's permutation/perturbation space (Lau et al., 5 Jul 2024).
• Zero-Shot and Minimal Resource Detection: Methods like DeCoMa highlight the need for watermarking techniques that withstand increasingly powerful abstraction-based or rewriting attacks, prompting research on stealthier or distributed-signal approaches (Xiao et al., 9 Apr 2025).
• Integration with Encrypted or Cryptographically-Protected Workflows: Seamless and efficient deployment of secure (e.g., ZKP-protected) watermark verification without usability drawbacks remains an open challenge (Zhang et al., 4 Feb 2025).

Adaptive code watermarking frameworks therefore represent a highly dynamic field, leveraging advanced techniques in code analysis, statistical detection, reinforcement learning, and cryptography to enforce IP protection, detect misuse, and ensure accountability of generative code artifacts.