Adaptive Dual-Stream Watermarking

• The paper introduces an adaptive dual-stream watermarking mechanism that employs parallel embedding channels with dynamic thresholding to enhance robustness against adversarial attacks.
• It leverages orthogonal methods like DWT-SVD, DCT-SVD in audio, dual-head strategies in LLMs, and dual-channel AST for code to ensure resilient and traceable watermark embedding.
• Empirical results demonstrate high performance with metrics such as <1% BER in audio and >97% bitwise accuracy in code, underscoring its practical applications in digital forensics and IP protection.

An adaptive dual-stream watermarking mechanism is an architectural and algorithmic paradigm used to embed and extract watermark signals by exploiting two complementary processing streams, with dynamic or context-aware adaptation at one or more stages of the watermarking workflow. This design provides heightened robustness against attacks, improved watermark detectability, and enhanced generalization across data modalities including audio, natural language, and source code. Distinct disciplinary instantiations exist for audio signals, LLM outputs, and code, yet all share core principles: multiple embedding channels operating in parallel or cascade, coordinated or adaptive thresholding strategies, and mutual reinforcement between streams to resist targeted removals or distortions.

1. Conceptual Foundations

Adaptive dual-stream watermarking mechanisms emerged from the need to overcome single-domain and static watermarking vulnerabilities. Early approaches such as the diverse domain (DWT-SVD & DCT-SVD) scheme for audio watermarking demonstrated that simultaneous embedding in two orthogonal transform domains significantly increased resilience to attack, with adaptively optimized detection thresholds (AOT/AOTx) dynamically compensating for channel distortion (Panachakel et al., 2017).

Later, with the growth of LLMs and concerns around paraphrase and spoofing attacks, dual-stream watermarking was extended with semantic adaptation. In DualGuard, two watermark "heads" are adaptively invoked based on content—enforcing both robustness and traceability under adversarial tampering (Li et al., 18 Dec 2025). For code, the dual-channel model in SrcMarker leverages both low-level (variable renaming) and high-level (AST rewrite) transformations to embed information in fundamentally different syntactic substrates, ensuring persistence under code transformations (Yang et al., 2023).

2. Formal Mechanisms and Architectures

The architecture of adaptive dual-stream watermarking mechanisms is predicated on parallel or cascaded branches—each embedding, modulating, or transforming the carrier data in a distinct representational domain or using a different set of semantic cues.

Audio Domain: DCT-SVD & DWT-SVD Cascade

• The audio signal is partitioned into frames.
• The first stream applies Discrete Wavelet Transform followed by SVD (DWT-SVD), embedding watermark bits in singular values of detail subbands.
• The second stream further processes the DWT-SVD output via Discrete Cosine Transform and SVD (DCT-SVD).
• A known pilot symbol is embedded in both streams for adaptive threshold calibration.
• On extraction, the process is reversed, with adaptive thresholding (AOT/AOTx) applied at each stage (Panachakel et al., 2017).

LLM Text Domain: Dual-Head Adaptive Watermarking

• Watermark injection operates at the logits level during generation.
• Two network heads (standard and adversarial) receive semantic embeddings, outputting tokenwise watermark signals.
• A similarity metric between heads determines, every $k$ tokens, which stream to apply based on semantic context—benign regions use the standard head; in regions flagged as anomalous or adversarial, the adversarial head is activated.
• The distance threshold $\alpha$ governs head switching, producing an adaptive, content-sensitive watermark signature (Li et al., 18 Dec 2025).

Code Domain: Dual-Channel AST and Natural Transformations

• Each source code function is transformed in two orthogonal channels:
  • Natural: variable renaming strategies encode bits via syntactic choices.
  • Formal: semantic-preserving AST rewrites encode bits at statement/block level.
• Embedding and extraction are orchestrated by neural modules coordinating rule selection; a feature-approximation module enables end-to-end differentiable training despite the non-differentiability of discrete AST rewrites.
• The two channels act as mutual backups for decoding, providing robustness to partial removals (Yang et al., 2023).

3. Adaptive Detection and Thresholding Strategies

Adaptivity is a core distinguishing feature, ensuring robust watermark presence even under unknown or channel-specific distortions.

Audio: AOT/AOTx Thresholding

• Adaptive thresholds are learned from pilot-embedded bits using max/min statistics on extracted SVD ratios, explicitly minimizing BER.
• AOTx extends AOT with a staged, error-minimizing search procedure to refine thresholds further, allowing real-time drift compensation under attack or transformation (Panachakel et al., 2017).

LLM and Code: Semantic and Structure-based Adaptation

• In LLM watermarking, head selection is conditioned on semantic divergence: flipping to the adversarial stream when content shifts indicate a potential spoofing event.
• For code, robustness is increased by randomizing rule selection and masking during training, thereby encouraging distributional diversity and resistance to uniform removal patterns (Li et al., 18 Dec 2025, Yang et al., 2023).

4. Robustness and Attack Resistance

Empirical robustness of adaptive dual-stream mechanisms appears across data modalities.

Audio

The dual-stream + adaptive thresholding scheme attains Bit Error Rate (BER) $<1\%$ (at $30$ dB channel SNR), with strong resilience to attacks (hum, amplification, delay, inversion, DCT-sparsification), consistently outperforming single-domain methods (Panachakel et al., 2017):

Attack	Dual-stream BER	DWT-SVD only	DCT-SVD only
50 Hz hum	<2%	8%	12%
14 dB amplification	<1%	5%	3%
100 ms delay	<3%	10%	18%
Signal inversion	<2%	7%	10%
DCT-sparsification	<5%	15%	25%

LLMs

DualGuard demonstrates state-of-the-art AUC and true-positive rates for watermark, paraphrase, and spoofing detection, with traceability functionality via stream-specific scores. Adaptive head switching underpins its ability to distinguish spoofed LLM text from both benign and malicious LLM output, with minimal impact on text quality metrics such as perplexity and downstream task performance (Li et al., 18 Dec 2025).

Code

SrcMarker's dual-channel embedding achieves high bitwise accuracy ($>97\%$ post-attack on CSN datasets) and maintains 99% syntax/unit-test pass rates—even under random renaming, AST rewrites, or partial watermark removal. Adaptive masking during training and dual-channel backup decoding underlie robustness (Yang et al., 2023).

5. Practical Applications and Evaluation Metrics

Audio

Applications include forensic audio tracking, copyright protection, and integrity verification. Metrics: Signal-to-noise ratio (SNR), subjective MOS, and per-attack BER.

LLM Output

Defensive watermarking for attribution, IP enforcement, and harmful content tracing. Key metrics: area under ROC (AUC), true-positive rate (TPR) at fixed false-positive rates (FPR), spoofing-trace scores, and downstream task fidelity (F1, ROUGE-L, EditSimilarity, perplexity) (Li et al., 18 Dec 2025).

Code

Software forensics, IP assertion, project-level verification (hypothesis testing against random bit extraction), and evaluation by BitAcc, MsgAcc, code BLEU, and CodeSearch MRR (Yang et al., 2023).

6. Comparative Advantages and Limitations

Adaptive dual-stream watermarking mechanisms offer enhanced resilience due to:

• Orthogonality of embedding domains or rule sets,
• Dynamic detector adaptation to environmental conditions,
• Content-aware head switching (LLMs),
• Mutual backup across channels under partial adversarial removal.

A plausible implication is that as adversaries develop more sophisticated removal techniques, adaptive and multi-stream mechanisms will remain fundamentally more robust than static or single-domain approaches. However, these methods may introduce additional computational and architectural complexity; some domains, e.g., source code or waveforms, require domain-specific parser or AST machinery for high-fidelity embedding and extraction.

7. Notable Implementations and Representative Systems

Domain	Method/System	Mechanism Summary	arXiv ID
Audio	DCT-SVD/DWT-SVD + AOT	Dual transforms, SVD-based embedding, adaptive pilot-based thresholding	(Panachakel et al., 2017)
LLM Text	DualGuard	Dual-head logits injection, adaptive semantic switching, spoofing traceability	(Li et al., 18 Dec 2025)
Code	SrcMarker	Dual-channel (name and AST), feature-approximation, flexible rule/backward pass	(Yang et al., 2023)

These systems demonstrate the generality of adaptive dual-stream watermarking across divergent modalities and substantiate its advantages over prior art in terms of robustness, transparency, and decoding accuracy.