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IWN: Image Watermarking Based on Idempotency

Kaixin Deng

TL;DR

The paper tackles the challenge of robust, reversible image watermarking by introducing the Idempotent Watermarking Network (IWN), inspired by the Idempotent Generative Network (IGN). It couples a DCT-based watermark embedding scheme with a U‑Net–like backbone and a multi-loss optimization that enforces reconstruction fidelity, idempotency, compactness, and watermark/original-image integrity, yielding a projection-mapping that remains stable under attack. Quantitative results on PSNR and SSIM demonstrate improved watermark recovery quality and resilience to damage, while the approach balances embedding capacity with robustness. This work offers a practical framework for robust copyright protection and information hiding, with avenues for single-step mappings, advanced embedding techniques, and more efficient architectures in future work.

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.

IWN: Image Watermarking Based on Idempotency

TL;DR

The paper tackles the challenge of robust, reversible image watermarking by introducing the Idempotent Watermarking Network (IWN), inspired by the Idempotent Generative Network (IGN). It couples a DCT-based watermark embedding scheme with a U‑Net–like backbone and a multi-loss optimization that enforces reconstruction fidelity, idempotency, compactness, and watermark/original-image integrity, yielding a projection-mapping that remains stable under attack. Quantitative results on PSNR and SSIM demonstrate improved watermark recovery quality and resilience to damage, while the approach balances embedding capacity with robustness. This work offers a practical framework for robust copyright protection and information hiding, with avenues for single-step mappings, advanced embedding techniques, and more efficient architectures in future work.

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.
Paper Structure (23 sections, 19 equations, 6 figures, 3 tables)

This paper contains 23 sections, 19 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Generative Networks
  4. Techniques for Watermark Processing
  5. Methods
  6. Method of Watermark Embedding
  7. Backbone Network
  8. Optimization Objective
  9. Reconstruction Loss
  10. Idempotency Loss
  11. Compactness Loss
  12. Watermark Image Loss
  13. Original Image Loss
  14. Experiments
  15. Training Parameters
  16. ...and 8 more sections

Figures (6)

  • Figure 1: The structure of the backbone network. Both the input and output images of the network are of size (128, 128).
  • Figure 2: A demonstration of the DCT algorithm.
  • Figure 3: Results of multiple projection mapping using the model. Even after processing 50 times using the IWN, the extracted watermarked image does not change much.
  • Figure 4: Heat map of the impact of mapping operations on image quality using the IWN model projection during the training phase.
  • Figure 5: Results of processing the attacked watermarked image using the IWN model. Here, zebra in the Set14 dataset is the original image to which we embedded the watermarked image ppt3.
  • ...and 1 more figures