Cascaded diffractive optical element for high-fidelity optical information encryption
Bibliographic record
Abstract
Cascaded diffractive optical element (DOE), consisting of multiple DOE layers, is a type of multi-layer architecture that introduces additional design freedom, e.g. rotation angle, wavelength or polarization state, enabling more flexible and precise modulation of light field compared to a single-layer DOE. This enhanced modulation capability endows it with significant potential for applications in the field of information encryption. For this application, the fidelity of image reconstruction is critically important to the performance of the cascaded DOE. In this work, we propose a new cascaded DOE design framework with the integration of an optimized Harvey’s model, enabling larger modulation bandwidth compared to conventional angular spectrum method (ASM), thereby increasing the information capacity of cascaded DOE, as well as the accuracy of reconstructed images. To validate the proposed method, we design a cascaded DOE for four distinct images encryption. The correlation coefficient of decrypted images is improved by 37% compared to the result that used ASM-based design method. Future work includes fabricating the designed DOE using a two-photon polymerization (2PP) technique and verifying its performance experimentally.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".