Residual Transferability in Neural Image Watermarking
Neural image watermarks can be forged by extracting watermark-bearing residuals from released images and transferring them to unrelated content. While prior work has demonstrated this vulnerability, what makes these residuals transferable remains poorly understood. We formalize this vulnerability with \textbf{residual transferability (RT)}, a metric that quantifies how well watermark evidence remains decodable after transfer across unrelated images. Through comparative analyses and controlled interventions, we find that common training-side variations do not account for the large RT differences across watermarking systems; instead, architectural design plays a central role. By contrasting...
| Authors | Ziping Dong, Qi Li, Xinchao Wang |
|---|---|
| Primary category | cs.CR |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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