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Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images

Xiaodan Xing, Huiyu Zhou, Yingying Fang, Guang Yang

TL;DR

This paper tackles the challenge of identifying AI-generated medical images while mitigating misuse by introducing an invisible watermark embedded via a frequency-domain DWT+DCT scheme into images produced by StyleGAN2 and Latent Diffusion Models. It evaluates the impact of watermarking on data augmentation utility using CheXpert and pediatric X-ray datasets, finding that fidelity, variety, FID, and privacy are largely preserved, though frequency-domain watermarking can influence cross-domain generalization. The study demonstrates frequency-domain watermark patterns can aid detectability without severely compromising intra-class utility, while highlighting potential limitations in outer-class applicability and the need for robust evaluation metrics. Overall, the work provides a foundation for ethical and legal discussions around watermark-based integrity and detection in medical image synthesis and suggests directions for future research on watermark design and evaluation frameworks.

Abstract

AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exhibit remarkable realism with their real copies, remains a concern. To mitigate this challenge, image generators such as DALLE and Imagen, have integrated digital watermarks aimed at facilitating the discernment of synthetic images' authenticity. These watermarks are embedded within the image pixels and are invisible to the human eye while remains their detectability. Nevertheless, a comprehensive investigation into the potential impact of these invisible watermarks on the utility of synthetic medical images has been lacking. In this study, we propose the incorporation of invisible watermarks into synthetic medical images and seek to evaluate their efficacy in the context of downstream classification tasks. Our goal is to pave the way for discussions on the viability of such watermarks in boosting the detectability of synthetic medical images, fortifying ethical standards, and safeguarding against data pollution and potential scams.

Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images

TL;DR

This paper tackles the challenge of identifying AI-generated medical images while mitigating misuse by introducing an invisible watermark embedded via a frequency-domain DWT+DCT scheme into images produced by StyleGAN2 and Latent Diffusion Models. It evaluates the impact of watermarking on data augmentation utility using CheXpert and pediatric X-ray datasets, finding that fidelity, variety, FID, and privacy are largely preserved, though frequency-domain watermarking can influence cross-domain generalization. The study demonstrates frequency-domain watermark patterns can aid detectability without severely compromising intra-class utility, while highlighting potential limitations in outer-class applicability and the need for robust evaluation metrics. Overall, the work provides a foundation for ethical and legal discussions around watermark-based integrity and detection in medical image synthesis and suggests directions for future research on watermark design and evaluation frameworks.

Abstract

AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exhibit remarkable realism with their real copies, remains a concern. To mitigate this challenge, image generators such as DALLE and Imagen, have integrated digital watermarks aimed at facilitating the discernment of synthetic images' authenticity. These watermarks are embedded within the image pixels and are invisible to the human eye while remains their detectability. Nevertheless, a comprehensive investigation into the potential impact of these invisible watermarks on the utility of synthetic medical images has been lacking. In this study, we propose the incorporation of invisible watermarks into synthetic medical images and seek to evaluate their efficacy in the context of downstream classification tasks. Our goal is to pave the way for discussions on the viability of such watermarks in boosting the detectability of synthetic medical images, fortifying ethical standards, and safeguarding against data pollution and potential scams.
Paper Structure (15 sections, 3 figures, 1 table)

This paper contains 15 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Data Synthesis Algorithms
  4. The Invisible Watermarking
  5. Image Evaluation
  6. Experiments
  7. Dataset
  8. Experimental Setting
  9. Results and Discussion
  10. Watermark Similarity as an Indicator of Image Fidelity in the Frequency Domain
  11. Watermarking Ensures Uncompromised Synthetic Data Quality
  12. Watermarking Has an Influence on Synthetic Data Utility
  13. Conclusion
  14. Compliance with ethical standards
  15. acknowledgments

Figures (3)

  • Figure 1: A graphical summary of our paper including our motivation of evaluating the quality and utility of water-marked synthetic images (a), and the method we used to analyze quality (b) and utility (c).
  • Figure 2: The visualization of watermarks using the DWT+DCT algorithm. All images are normalized to $[0,1]$.
  • Figure 3: Comparison of accuracy improvement for two evaluation conditions on dataset C (same clinical question) and D (different clinical question). Blue bars represent results when using internal augmentation (StyleGAN synthesized A1 and LDM synthesized A1), and red bars represent results with external augmentation (StyleGAN synthesized A2 and LDM synthesized A2). * represents $p<0.05$ according to McNemar's test. The y-axis is zero-normalized based on the test accuracy of the model trained solely on A1 to provide a clearer comparison.