A Novel Chaos-Based CDT Map for Digital Image Encryption
Bibliographic record
Abstract
Securing digital images remains a major challenge in modern communication systems due to increasing risks of interception, manipulation, and unauthorized access.This study proposes a novel chaotic function called the Circle-Dyadic Transformation Map (CDT Map), constructed through the composition of the Circle Map and the Dyadic Transformation Map, to serve as a keystream generator for image encryption.The methodology consists of four key stages: (1) formulating the CDT Map via function composition, (2) validating its chaotic behavior through Lyapunov exponent analysis, bifurcation diagrams, and the NIST SP800-22 test suite, (3) designing a keystream-based encryption-decryption algorithm using XOR operations, and (4) evaluating performance through statistical, differential, and quality metrics.Experimental results show that the CDT Map achieves a 100% pass rate on NIST randomness tests, a key space of 5.832 × 10⁶⁵⁰, high key sensitivity (10⁻¹⁶), and superior NPCR (99.6%) and UACI (≈40%) values compared to existing chaotic maps.The proposed approach ensures perfect decryption (MSE = 0, PSNR = ∞) and strong resistance to brute-force, statistical, and differential attacks.The main contribution of this work is the development of a new composite chaotic function that significantly enhances randomness, security strength, and computational efficiency for digital image encryption.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".