Hiding Secrets in Electrocardiogram Based on Integer Wavelet Transform with Coefficient Adjustment and LSB Substitution
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
In this paper, we present an efficient data hiding for electrocardiogram (ECG) hosts to solve the issues of existing ECG steganographic methods, which have less hiding capacity and insufficient signal-to-noise ratio (SNR)/ peak SNR (PSNR). Based on the integer wavelet transform (IWT), private (or sensitive) data such as patients’ diagnosis and personal information can be embedded in an ECG host via the IWT coefficient adjustment and the least significant bit (LSB) technique. Simulations indicated that the SNR (or PSNR), and payload of the proposed method outperform those of existing techniques. In addition, the proposed method is capable of resisting attacks, such as cropping, Gaussian noise-addition inversion, scaling, translation, and truncation attacks from third parties (or adversaries). Since the proposed method has low computational complexity, our method can be employed in portable biometric devices or wearable electronics.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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