Qingyuan Yu

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.