Stream ciphers for digital image transactions by learning quantum true random numbers
Bibliographic record
Abstract
Abstract The digital economy drives a surge in online digital image transactions, increasing the risk of data breaches due to extensive image file transmission. Stream ciphers, known for their efficiency compared to block ciphers, have emerged as a preferred choice for encrypting images in such transactions to safeguard transmitted data. Nevertheless, traditional stream cipher algorithms face diverse security threats. To address this challenge, efforts have been devoted to generating stream ciphers by generative adversarial networks (GANs) transforming input style into random patterns. Regrettably, these ciphers face issues in key sensitivity, randomness, and style transformation failures. Quantum true random numbers offer a potential solution but are costly to deploy. To handle this dilemma, we design stream ciphers relied on a neural network random number generator (RNG) using quantum true random numbers for training least squares GANs. Specifically, two fully-connected layers are incorporated into the RNG, avoiding the defects of style transformation in existing GANs-based stream ciphers. Besides, a random number calculation formula is employed to ensure that each decimal place output by the generator contributes to the computation of the random numbers. By doing so, the randomness of GANs is enhanced and the deployment of costly quantum devices is avoided. Experiments reveal that the information entropy of our generated images reaches to 7.9991, the adjacent pixel correlation coefficient of the ciphertext attains -0.0015, the Number of Pixel Change Rate and Unified Average Changing Intensity achieve 99.62% and 33.52%, respectively. These results demonstrate that the designed RNG facilitates randomness, whilst having secure properties applied in stream ciphers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".