Alignment Is Not Enough: Preserving Message Separability for Geometrically Robust Image Watermarking

Invisible watermarking embeds a traceable identifier in an image and must recover it after routine geometric processing in real-world use. Resampling attenuates message-bearing components, while strengthening the signal harms visual quality, so the identifier must remain separable from competing candidates under both pressures. Existing systems recover under some of these conditions but collapse under others, and we therefore design the system around separability. Our readout matches each template over its orbit of integer feature shifts, absorbing alignment variation without estimating it. Because no alignment search can recover components that interpolation has erased, the codebook excludes the spectral component that vanishes under half-bin interpolation. A two-stage embedding then places the message in the host image, first balancing residual energy against message margins, then refining perceptual quality at fixed energy while protecting geometric margins. Across nine baselines and three datasets in closed-set 64-ID identification, ours is the only system whose mean recovery exceeds 99.5% in every evaluated condition, while a synchronization-corrected baseline falls as low as 60% when geometry is composed with JPEG. Ours reaches 99.92% off-grid ID accuracy at LPIPS 0.00946, retains 99.90% after a PNG codec round trip, and accepts no wrong ID across 18 watermarked conditions at a 0.04 rejection threshold.