IWN: Image Watermarking Based on Idempotency

TL;DR

This paper introduces the Idempotent Watermarking Network (IWN), a neural network that enhances image watermark recovery and robustness by integrating idempotency, improving watermark quality and reversibility in digital media security.

Contribution

The paper proposes a novel neural network model, IWN, that incorporates idempotency to improve watermark recovery and robustness, addressing limitations of traditional watermarking methods.

Findings

IWN achieves higher watermark recovery quality.

The model balances embedding capacity and robustness effectively.

Enhanced resistance to watermark damage and attacks.

Abstract

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.