Tracing model-generated DNA with position-independent watermarking
q-bio.GN, cs.CR
Submitted: 2026-10-03
Updated: 2026-10-03
Comments: 16 pages
Code: https://github.com/google-deepmind/synthid-text
License: http://creativecommons.org/licenses/by/4.0/
The gist: Genomic language models can write synthetic DNA that carries no record of its origin.
Abstract
Genomic language models can write synthetic DNA that carries no record of its origin. A generation-time watermark could provide a provenance signal, if a verifier can detect it using only the DNA sequence, a key, and published detector settings, without knowing where the generated region starts, which strand it lies on, or how it was divided into six-base tokens. We embed the SynthID tournament watermark in two genomic language models, Carbon and GENERator-v2. The verifier searches both strands, every start position, and four window lengths, and corrects its decision for the whole search. In a detection cohort of 1,544 held-out prompts, it found all 3,088 marked sequences per model, unedited and after one substituted, inserted, or deleted base. For ordinary sequences, the one-sided 95% upper confidence bound on the false-positive rate was at most 0.850%; one bound for the wrong-key control reached 1.015% (Carbon, after a deletion). In a development cohort, neither model showed a detectable change in likelihood or in predefined sequence measures. Under random edits placed without reference to the detector, detection stayed complete or nearly complete up to a 2% per-base edit rate. These results show statistical detection, not biological function, secret-key security, or robustness to an editor who sees the detector.
Sources
- Undetectable Watermarks for Language Models
- Edit Distance Robust Watermarks via Indexing Pseudorandom Codes
- Robustness Assessment and Enhancement of Text Watermarking for Google's SynthID
- Unbiased Watermark for Large Language Models
- Watermark Stealing in Large Language Models
- A Watermark for Large Language Models
- On the Reliability of Watermarks for Large Language Models
- Robust Distortion-free Watermarks for Language Models
- GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models
- BEND: Benchmarking DNA Language Models on biologically meaningful tasks
- HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
- On Google's SynthID-Text LLM Watermarking System: Theoretical Analysis and Empirical Validation
- DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA
- Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
- GENERator: A Long-Context Generative Genomic Foundation Model
- A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models
- Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models
- Securing the Language of Life: Inheritable Watermarks from DNA Language Models to Proteins
- Provable Robust Watermarking for AI-Generated Text
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