Design and Hardware Implementation of A Separable Image Steganographic Scheme using Public-key Cryptosystem
Bibliographic record
Abstract
In this paper, a novel and efficient hardware implementation of steganographic cryptosystem based on a public-key cryptography is proposed.Digital images are utilized as carriers of secret data between sender and receiver parties in the communication channel.The proposed public-key cryptosystem offers a separable framework that allows to embed or extract secret data and encrypt or decrypt the carrier using the public-private key pair, independently.Paillier cryptographic system is adopted to encrypt and decrypt pixels of the digital image.To achieve efficiency, a proposed efficient parallel montgomery exponentiation core is designed and implemented for performing the underlying field operations in the Paillier cryptosystem.The hardware implementation results of the proposed steganographic cryptosystem show an efficiency in terms of area (resources), performance (speed) and power consumption.Our steganographic cryptosystem represents a small footprint making it well-suited for the embedded systems and real-time processing engines in applications such as medical scanning devices, autopilot cars and drones.
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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) | 0.002 | 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".