发表机构
University of Geneva（日内瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NAWE结合显式信号处理水印与预训练神经宿主预测器，通过周期载波和Polar编码实现鲁棒提取，在多种攻击下取得最低误码率。
AI 中文摘要
NAWE（神经辅助水印提取）将显式信号处理水印构造与预训练的神经宿主预测器相结合。周期性、感知掩蔽的水印载波提供同步，Polar编码提供冗余，去噪后减去提取嵌入的水印。去噪器保持冻结，不进行水印特定训练。一项单因素研究比较了Wiener、BM3D、DRUNet和GS-DRUNet宿主估计器。与TrustMark、SSL Watermarking、PixelSeal和WAM的比较显示，NAWE在几何和光度类别上的误码率最低，且消息恢复能力强，而滤波和噪声仍存在限制，这与水印提取器的非自适应选择一致。比较保留了各系统不同的有效载荷和编码。
英文摘要
NAWE (Neural-Assisted Watermark Extraction) combines an explicit signal-processing watermarking construction with a pretrained neural host predictor. A periodic, perceptually masked watermark carrier provides synchronization, Polar coding supplies redundancy, and denoising followed by subtraction extracts the embedded watermark. The denoiser remains frozen, without watermark-specific training. A one-factor-at-a-time study compares Wiener, BM3D, DRUNet, and GS-DRUNet host estimators. Comparisons with TrustMark, SSL Watermarking, PixelSeal, and WAM show NAWE's lowest geometric and photometric class BER and strong message recovery, while filtering and noise remain limitations consistent with the non-adaptive selection of the watermark extractor. The comparison retains the systems' different payloads and coding.
