ECDSA-based tamper detection in medical data using a watermarking technique
Bibliographic record
Abstract
Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into the image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the original image. Moreover, the SSIM reached 0.98, denoting a nearly perfect resemblance between the watermarked and original images. The suggested approach also demonstrated quick embedding and extraction speeds, as well as tamper detection capabilities.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".