Symmetric Image Encryption Using Chaotic Logistic Map and Deep Convolutional Feature Learning
Bibliographic record
Abstract
The rapid increase in the transmission and storage of digital images has intensified the need for encryption algorithms that ensure visual confidentiality and resilience against statistical and differential attacks.Conventional encryption approaches often struggle to eliminate residual structural information, particularly when handling highly correlated image data.To overcome these limitations, this study proposes a hybrid symmetric image encryption method that combines the unpredictability of chaotic logistic map operations with the deep representational capabilities of convolutional autoencoders.The encryption process consists of a two-stage mechanism: first, the image undergoes chaotic pixel permutation, substitution, and XOR masking; second, the result is passed through a deep convolutional network for feature-level obfuscation, further diminishing any remaining visual patterns.The proposed method was evaluated on multiple standard grayscale images using four key metrics: MSE, PSNR, UACI, and NPCR.The averaged results across all test images show an MSE of 36.23, a PSNR of 7.46 dB, a UACI of 33.50%, and an NPCR of 99.60%.These values indicate strong encryption quality and high sensitivity to plaintext variations.The integration of chaotic systems with deep learning effectively enhances security while maintaining computational efficiency, providing a robust solution for secure visual data protection in modern applications.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".