Next-Generation Secure and Reversible Watermarking for Medical Images using Hybrid Radon-Slantlet Transform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research article proposes a security-enhanced watermarking method for medical images using the Radon and Slantlet transforms. The first step involves transforming the cover image from the spatial domain to the Radon domain. This transformation involves rotation, scaling, and translation through the Radon transform, which alters the locations of concealed bits. As a result, identifying the embedded data poses a considerable challenge. The embedded data cannot be identified without employing the inverse Radon transform. Subsequently, the Radon-transformed image is converted to the frequency domain using the Slantlet transform. Secret bits are incorporated into frequency coefficients during this phase through the pixel pair mapping approach. The final watermarked image is generated by inserting side information into the robust watermarked image. Simulation experiments are carried out to evaluate the imperceptibility of watermarks in medical images, employing metrics such as PSNR and SSIM. The results indicate high imperceptibility, with PSNR values exceeding 45 dB and SSIM values surpassing 0.95 for all tested images. Furthermore, the proposed method's robustness and reversibility are assessed by exposing watermarked images to various attacks. Performance is measured through the BER and NCC. Experimental findings reveal a BER of 0.2 % for the watermarked information, indicating strong resilience against attacks. Additionally, the NCC is determined to be 0.96, highlighting a high level of reversibility in extracting embedded data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it