IWN：基于幂等性的图像水印
IWN: Image Watermarking Based on Idempotency
AI总结：
本文受幂等生成网络启发，提出幂等水印网络IWN，利用幂等性提升彩色图像水印恢复质量，并在嵌入容量与鲁棒性之间取得平衡。
摘要：
在数字媒体不断扩展的领域中，保持水印技术的强度和完整性正变得越来越具有挑战性。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）。所提出的模型专注于提升彩色图像水印的恢复质量，利用幂等性来确保卓越的图像可逆性。这一特性确保即使彩色图像水印受到攻击或损坏，它们也能被有效地投影并映射回原始状态。因此，提取出的水印质量无疑得到了提高。IWN模型在嵌入容量和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的固有矛盾。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
