WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck

Robust invisible watermarking is essential for copyright protection, yet generative editing severely threatens watermark survival by rewriting host-image details. Our quantitative analyses reveal that existing watermark representations become deeply entangled with host-speciffc textures, causing generative modiffcations to disrupt message decoding. To break this entanglement, we formulate watermark extraction as learning a minimal sufffcient statistic for message recovery, thereby substantially reducing the decoder’s reliance on fragile host textures. Guided by this insight, we propose WaterVIB, a plug-in variational bottleneck module that decouples message representations from host-image details across existing backbones. Systematic evaluation across ffve 2D backbones with different architectures under 32 attacks conffrms widespread robustness gains thanks to our WaterVIB. On VINE-R, WaterVIB reduces the bit error rate under global puriffcation by 71.0% while increasing embedding PSNR. Furthermore, extending WaterVIB to NeRF-Signature enhances 3D rendering ffdelity while preserving clean decoding performance.