A Novel Integration of Proximal Policy Optimization, In-Memory Computing and Visual Cryptography for Secure Image Encryption
Bibliographic record
Abstract
This paper presents a pioneering framework that synergizes Proximal Policy Optimization (PPO) with in-memory computing (IMC) and visual cryptography (VC) to achieve high-throughput, energy-efficient and computation-free secure image encryption.Leveraging a custom Phase-Change Memory (PCM) based IMC prototype, we implement PPO to optimize encryption policies under resource constraints and apply VC to generate secret shares readable by the human visual system without cryptographic decoding.Experiments employ the publicly available MNIST dataset (https://yann.lecun.com/exdb/mnist/)and the CIFAR-10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html) to validate both grayscale and color scenarios.PPO learns optimal memory access and cryptographic parameter settings, reducing energy consumption by 37% and latency by 42% compared to baseline reinforcement learning methods.VC shares are produced with zero pixel expansion, achieving a mean Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, outperforming traditional Naor-Shamir VC by 15% in image quality metrics.A comparative analysis with recent VC schemes and IMC encryption architectures highlights that our framework fills gaps in scalable, computation-free decryption and adaptive security policy learning, rendering it practical for edge devices.All results are derived from precise in situ measurements and validated formulas.This work delivers a novel, validated and industry-relevant contribution to secure computing and visual cryptography research.[1][2][3
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".