Rad-Mark: Reliable adversarial zero-watermarking

doi:10.1016/j.neucom.2025.129970

2025-07-01

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DOI：10.1016/j.neucom.2025.129970

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零水印是一种无损保护技术，因此它被广泛应用于需要无损保护的医学图像、艺术品和其他载体。然而，当前的零水印存在不同宿主图像特征图像之间相似度过高的问题，导致误报率较高。为解决这一挑战，我们提出Rad-Mark，这是一种基于深度学习的零水印框架，首次利用对抗特征优化显著增强水印检测的鲁棒性和准确性。对抗样本被用于显著提高框架的安全性，其误报的NC值可接近0.5。图像扰动和高斯噪声均被纳入训练过程。具体而言，我们的Rad-Mark包含特征融合设计，以及基于局部滤波特征和全局手工特征融合的映射网络。我们对关键参数进行了深入分析，包括高斯噪声、水印维度和权重因子，探索了它们对Rad-Mark性能的影响。大量实验结果表明，Rad-Mark在安全性和鲁棒性方面均优于现有的零水印方法。

Keyword:

Zero-watermarking

Adversarial training

Local-global features

False positive

Security and robustness

期刊

Neurocomputing

IF:

6.5

论文数:

2.5W

被引数:

6.5W

机构

Univ North Carolina Chapel Hill

学者数:

1.5K

论文数: 760

被引数: 228

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