Problem
Research questions and friction points this paper is trying to address.
Digital Watermarking
Watermark Extraction
Noise
Filtering
Information Recovery
NAWE: Digital Watermarking with Neural-Assisted Watermark Extraction
🤖 AI Summary
NAWE通过结合信号处理水印构建与预训练神经网络主机预测器，利用周期性水印载体、极化编码及去噪减法提取嵌入水印，以改善数字水印提取效果。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neural-Assisted Watermark Extraction
Polar coding
Denoiser
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