MoiréComm: Secure Screen-Camera Communication Based on Moiré Cryptography
Bibliographic record
Abstract
Quick Response (QR) codes have become increasingly popular for screen-camera communication due to their swift readability and widespread smartphone use. Nevertheless, they are vulnerable to privacy invasions from unauthorized photography. Addressing this, we propose a novel Moiré encryption technique-based secure screen-camera communication system, named MoiréComm. The Moiré encryption can enhance security by using distinct spatial frequency patterns for camouflage. The original QR code is revealed as a Moiré pattern only when the camera in a designated position, e.g., directly in front and 30 cm from the screen. From any other positions, only the camouflaged QR code can be seen. Decryption schemes are customized for different scenarios. The multi-frame approach achieves a decryption success of over 98.6% within 13.2 frames in handheld scenarios. Conditional generative adversarial network (cGAN)-based decryption method decodes the Moiré QR code images with a 98.8% success rate in 0.02 s within three frames and is also applicable in handheld scenarios. For fixed screen-camera setups, our fast decryption scheme achieves 99.4% success within two frames, with average 0.4 s latency. Significantly, the decryption rate plunges to 0% for surveillance cameras displaced by 20$^\circ$or more than$\ge$10 cm from the target position, demonstrating MoiréComm's resilience against attacks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".