An Enhanced NPCR (ENPCR) Metric with Improved Image/ Video Frame Security
Bibliographic record
Abstract
Transmitted data face many challenges during transmission to achieve data security. Securing transmitted multimedia data can be obtained through images and videos encryption for confidentiality purposes. Hackers usually try to implement several attacks including differential attack to get the original plaintext image/ video frame through revealing the encryption key. Evaluation metrics are applied to test the robustness of the applied encryption algorithm against various attacks. Metrics are based on statistical measures such as number of pixels change rate (NPCR). Image contrast is not sensitive while changing pixel intensity few values and the resulting image may still reveal the details of the original image. For example, applying encryption using Caesar Cipher with a small key value such as 1 or 2. Nevertheless, the NPCR of the resulted image is 100% which is the ultimate goal. This is a considerable flaw and resulted from the core function of the NPCR equation. This research proposes an enhancement to the NPCR metric based on the difference of intensity values introducing enhanced NPCR (ENPCR). Linear and non-linear core functions were proposed and tested instead of the current binary core function to provide a better NPCR performance. The results presented show that approaching non-linear core functions are more accurate than utilizing a linear function. On the other hand, the processing time and complexity of a linear function makes it preferable.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".