A 3D-DWT-Based Fragile Watermarking Framework for Volumetric Medical Image Authentication and Tamper Detection

Abstract

The integrity of medical imaging data is essential to trust diagnostically accurate results and make correct decisions about patient treatment. However, recent advances in deep learning-based image manipulation have revealed critical vulnerabilities in existing medical image workflows. In particular, volumetric (3D) medical images, which are stored as DICOM files, are vulnerable to undetectable (invisible to the naked eye) tampering, which will not be recognizable to a human or passively detectable by any means. This paper describes an active fragile authentication method for 3D medical images, using a valid 3D Discrete Wavelet Transform (DWT). The proposed technique embeds a cryptographically seeded fragile watermark within selected high-frequency volumetric sub-bands, thus providing sensitive detection of content-based tampering while preserving the diagnostic quality of the image. Unlike slice-wise or metadata-based techniques, the proposed method works directly on all 3D volumetric images as one unified volume, which promotes spatial integrity across slices and protects against format-based tampering. Authentication is performed using a block-based correlation analysis method that enables sensitive detection of voxel-level changes, even if the changes are slight. The evaluation results demonstrate that the proposed technique provides very high levels of invisibility (i.e., peak signal-to-noise ratios of greater than 56 dB), while still maintaining very effective authentication capabilities in the presence of noise-based tampering. These results indicate that the proposed method provides an effective and practical solution for safeguarding the integrity of 3D medical imagery in clinical environments.