STBCIoT: Securing the Transmission of Biometric Images in Customer IoT
Bibliographic record
Abstract
The recent advancement of the Internet of Things (IoT) and information technology has led to the rapid expansion of interconnectivity among a billion devices across various applications. The advent of massive data has resulted in greater computational dependence, posing obstacles to applying security policies in energy-sensitive devices. However, public-key-based encryption algorithms are impractical or impossible to execute on these resource-limited terminals. In this paper, we propose a lightweight framework called STBCIoT based on a visual cryptography (VC) scheme to achieve low-latency encryption for large-scale data like biometric images. To reduce noise in encryption, we utilize central recognition and gray-level features of QR codes to integrate the visually friendly feature of QR into VC. we further propose a high-quality image generation model with the halftoning effect of VC to improve the quality of decrypted images. The experimental results demonstrate that our proposed method achieves high recognition performance on lossy decrypted images, effectively overcoming the performance limitations of traditional public key encryption methods for largescale images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".