IWN: Image Watermarking Based on Idempotency
This addresses the problem of watermark robustness and reversibility for digital media protection, representing an incremental improvement over traditional techniques.
The paper tackles the challenge of maintaining watermark strength and integrity in digital media by proposing the Idempotent Watermarking Network (IWN), which enhances recovery quality of color image watermarks through idempotency, ensuring effective projection and mapping back to the original state even after attacks.
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.