利用幂等性提升图像水印恢复质量，增强鲁棒性。
IWN: Image Watermarking Based on Idempotency
- 引入幂等性机制，实现水印的可逆恢复
- 在嵌入容量与鲁棒性间取得更好平衡
- 适合需要高可靠性水印的数字版权保护场景
在数字媒体快速发展的背景下，保持水印技术的强度和完整性面临日益严峻的挑战。本文受幂等生成网络（IGN）启发，探索将幂等性引入图像水印处理，并提出一种创新的神经网络模型——幂等水印网络（IWN）。该模型聚焦于提升彩色图像水印的恢复质量，利用幂等性确保优异的图像可逆性。即使彩色图像水印遭受攻击或损坏，也能有效投影并映射回原始状态，因此提取出的水印质量显著提高。IWN 模型在嵌入容量与鲁棒性之间实现了良好平衡，缓解了传统水印技术和隐写术中两者固有的矛盾。
原文摘要 · Abstract (English)
In the expanding field of digital media, maintaining the strength and integrity of watermarking technology is becoming increasingly challenging. This paper, inspired by the Idempotent Generative Network (IGN), explores the prospects of introducing idempotency into image watermark processing and proposes an innovative neural network model - the Idempotent Watermarking Network (IWN). The proposed model, which focuses on enhancing the recovery quality of color image watermarks, leverages idempotency to ensure superior image reversibility. This feature ensures that, even if color image watermarks are attacked or damaged, they can be effectively projected and mapped back to their original state. Therefore, the extracted watermarks have unquestionably increased quality. The IWN model achieves a balance between embedding capacity and robustness, alleviating to some extent the inherent contradiction between these two factors in traditional watermarking techniques and steganography methods.
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