Why Neural Structural Obfuscation Can't Kill White-Box Watermarks for Good!
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908883683901440 |
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| author | Jiang, Yanna Yu, Guangsheng Yu, Qingyuan Chen, Yi Wang, Qin |
| author_facet | Jiang, Yanna Yu, Guangsheng Yu, Qingyuan Chen, Yi Wang, Qin |
| contents | Neural Structural Obfuscation (NSO) (USENIX Security'23) is a family of ``zero cost'' structure-editing transforms (\texttt{nso\_zero}, \texttt{nso\_clique}, \texttt{nso\_split}) that inject dummy neurons. By combining neuron permutation and parameter scaling, NSO makes a radical modification to the network structure and parameters while strictly preserving functional equivalence, thereby disrupting white-box watermark verification. This capability has been a fundamental challenge to the reliability of existing white-box watermarking schemes.
We rethink NSO and, for the first time, fully recover from the damage it has caused. We redefine NSO as a graph-consistent threat model within a \textit{producer--consumer} paradigm. This formulation posits that any obfuscation of a producer node necessitates a compatible layout update in all downstream consumers to maintain structural integrity. Building on these consistency constraints on signal propagation, we present \textsc{Canon}, a recovery framework that probes the attacked model to identify redundancy/dummy channels and then \textit{globally} canonicalizes the network by rewriting \textit{all} downstream consumers by construction, synchronizing layouts across \texttt{fan-out}, \texttt{add}, and \texttt{cat}. Extensive experiments demonstrate that, even under strong composed and extended NSO attacks, \textsc{Canon} achieves \textbf{100\%} recovery success, restoring watermark verifiability while preserving task utility.
Our code is available at https://anonymous.4open.science/r/anti-NSO-9874. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12679 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Why Neural Structural Obfuscation Can't Kill White-Box Watermarks for Good! Jiang, Yanna Yu, Guangsheng Yu, Qingyuan Chen, Yi Wang, Qin Cryptography and Security Neural Structural Obfuscation (NSO) (USENIX Security'23) is a family of ``zero cost'' structure-editing transforms (\texttt{nso\_zero}, \texttt{nso\_clique}, \texttt{nso\_split}) that inject dummy neurons. By combining neuron permutation and parameter scaling, NSO makes a radical modification to the network structure and parameters while strictly preserving functional equivalence, thereby disrupting white-box watermark verification. This capability has been a fundamental challenge to the reliability of existing white-box watermarking schemes. We rethink NSO and, for the first time, fully recover from the damage it has caused. We redefine NSO as a graph-consistent threat model within a \textit{producer--consumer} paradigm. This formulation posits that any obfuscation of a producer node necessitates a compatible layout update in all downstream consumers to maintain structural integrity. Building on these consistency constraints on signal propagation, we present \textsc{Canon}, a recovery framework that probes the attacked model to identify redundancy/dummy channels and then \textit{globally} canonicalizes the network by rewriting \textit{all} downstream consumers by construction, synchronizing layouts across \texttt{fan-out}, \texttt{add}, and \texttt{cat}. Extensive experiments demonstrate that, even under strong composed and extended NSO attacks, \textsc{Canon} achieves \textbf{100\%} recovery success, restoring watermark verifiability while preserving task utility. Our code is available at https://anonymous.4open.science/r/anti-NSO-9874. |
| title | Why Neural Structural Obfuscation Can't Kill White-Box Watermarks for Good! |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2603.12679 |