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.

翻译：在数字媒体领域不断扩展的背景下，保持水印技术的鲁棒性与完整性正变得日益具有挑战性。本文受幂等生成网络（IGN）的启发，探讨了将幂等性引入图像水印处理的前景，并提出了一种创新的神经网络模型——幂等水印网络（IWN）。该模型以提高彩色图像水印的恢复质量为核心，利用幂等性确保卓越的图像可逆性。这一特性保证了即使彩色图像水印遭受攻击或损坏，也能被有效地投影并映射回原始状态。因此，提取出的水印质量得到了显著提升。IWN模型在嵌入容量与鲁棒性之间实现了平衡，在一定程度上缓解了传统水印技术和隐写方法中这两个因素之间固有的矛盾。