aiqbench analysis
This paper proposes Tokenization-Invariant Watermarking (TI-WM), a method that embeds watermarks during token generation but evaluates them on canonicalized UTF-8 bytes to eliminate dependency on specific tokenizer implementations. The authors demonstrate improved resilience against white-box attacks with word edits while highlighting inherent limitations regarding visible insertion outside the canonicalizer's deletion set and unresolved source shift issues. This work advances AI safety by offering a concrete construction for more robust content provenance detection in large language models.
Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text
arXiv:2610.14378v1 Announce Type: cross Abstract: We introduce Tokenization-Invariant Watermarking (TI-WM), a method that embeds a watermark during token generation but keys and scores it on canonicalized UTF-8 bytes. The detector does not invoke the tokenizer. Canonicalizer-preserving edits leave its score unchanged; other localized canonical-byte edits affect a bounded number of scoring windows. Across three fixed keys and 128 prompts per key and model, a deterministic white-box attack at a 5% word-edit budget leaves mean attacked-AUC 0.530 on Qwen3-8B and 0.664 on Gemma 4 E4B, compared with 0.410 and 0.489 for Unigram with detection-side deletion. These operating points are not jointly matched on quality and clean detectability. Bias sweeps show model-dependent distortion costs, and shared-rule controls support a locality interpretation without establishing a unique advantage of bytes. Visible insertion outside the canonicalizer's deletion set defeats the default design. Alphabetic filtering closes that channel but does not resolve source shift or calibrated detection power. TI-WM thus provides a concrete construction with local scoring guarantees and measured attack-ranking gains, not a general robustness or low-false-positive-rate deployment guarantee.
Abstract:We introduce Tokenization-Invariant Watermarking (TI-WM), a method that embeds a watermark during token generation but keys and scores it on canonicalized UTF-8 bytes. The detector does not invoke the tokenizer. Canonicalizer-preserving edits leave its score unchanged; other localized canonical-byte edits affect a bounded number of scoring windows. Across three fixed keys and 128 prompts per key and model, a deterministic white-box attack at a 5% word-edit budget leaves mean attacked-AUC 0.530 on Qwen3-8B and 0.664 on Gemma 4 E4B, compared with 0.410 and 0.489 for Unigram with detection-side deletion. These operating points are not jointly matched on quality and clean detectability. Bias sweeps show model-dependent distortion costs, and shared-rule controls support a locality interpretation without establishing a unique advantage of bytes. Visible insertion outside the canonicalizer's deletion set defeats the default design. Alphabetic filtering closes that channel but does not resolve source shift or calibrated detection power. TI-WM thus provides a concrete construction with local scoring guarantees and measured attack-ranking gains, not a general robustness or low-false-positive-rate deployment guarantee.
Cryptography and Security (cs.CR); Computation and Language (cs.CL)
arXiv:2610.14378 [cs.CR]
(or arXiv:2610.14378v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2610.14378 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 268 words
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