Enhancing Biometric Fingerprint Security Through Integrated Watermarking and Cipher Block Chaining Techniques
Bibliographic record
Abstract
This study presents a combined watermarking-cryptography approach to bolster the security of biometric fingerprint data.In the initial stage, a digital watermark is embedded within the host image (fingerprint scan) using the Least Significant Bit (LSB) technique.Upon completion of the embedding process, the watermarked image undergoes encryption using the Advanced Encryption Standard (AES) in Cipher Block Chaining (CBC) mode, increasing complexity and ensuring secure communication.Conversely, the extraction procedure involves reversing the embedding steps by first decrypting the received image and subsequently applying the extraction algorithm to the unencrypted image to recover the embedded watermark.The proposed method demonstrates significant imperceptibility, as measured by the Peak Signal-to-Noise Ratio (PSNR) and Normalized Correlation (NCC) metrics.Furthermore, the watermark exhibits resilience against signal processing attacks, including noise and filtering.The heightened key sensitivity of the CBC cryptosystem renders the proposed scheme more resistant to statistical attacks compared to existing methods in the literature.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".