A Visual Cryptography Framework with Tuned Cipher Block Chaining and Quantum Key Distribution–Assisted Encryption for Securing Thermal Facial Biometrics in Anti-Doping Applications
Bibliographic record
Abstract
In this paper, Visual Cryptography (VC) is applied to the thermal images of players acquired in the sports field.The objective of VC is to protect players' information during a dope test done through thermal image analysis.VC transfers the thermal image in secured channel.The thermal image biomarker for a dope test is elevated skin temperature, asymmetrical heat patterns, excessive muscle heat retention, and abnormal recovery thermal signature.However, a major problem is the prevention of thermal images from the data breach, such as privacy violations, and the manipulation of doping assessments.To address the above problems, the player's thermal image is applied with VC with a quantum key algorithm and secures the player's identity.In the proposed method, the tampered thermal image is identified through the abnormal heat distribution in the player's face in the image.Initially, the thermal image is pre-processed using the Adaptive Histogram Equalization (AHE) and denoised using the Gaussian filter.Next, the image is divided into two secret shares, followed by the encryption and decryption process using the proposed Tuned Cipher Block Chaining with Quantum Key Distribution (TCBCQD) technique.The number of shares is decided by the TCBCQD technique.The number of shares is the tuning method in the proposed TCBCQD technique.Cryptographic-based access is done by the anti-doping agencies.The original image is deciphered after combining both the shares, which are available from the higher authorities.The image quality and security metrics were obtained.The proposed TCBCQD technique reconstructs the image with an accuracy rate of 98% and outperforms the existing methodologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".