Novel Dual-Domain Chaotic Image Cryptosystem for Cybersecurity Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The secrecy of various forms of multimedia data constitutes a significant aspect of the cybersecurity field. In this case, chaos-based image ciphers can be adapted. Chaotic image encryption has been investigated in the literature using different transformation domains, such as spatial, discrete Fourier transform, wavelet transform, etc. Although some frequency-based cryptosystems exhibit favorable confusion properties, they may suffer in terms of diffusion properties and fail in terms of some statistical characteristics, which make them vulnerable to various statistical, analytical, and differential attacks. In this regard, we adopt a dual-domain (wavelet and spatial domains) image cryptosystem and propose a novel diffusion process in the wavelet domain to address the problem of weak resilience against the aforementioned attacks. The proposed diffusion process in the wavelet domain is applied only on 1/16 of the pixels of the plain image, which makes it computationally more efficient compared to the existing wavelet domain-based works. In addition, the proposed cryptosystem solves the bell shape histogram problem associated with some frequency-based cryptosystems, which has been verified using different performance metrics in our simulation results. We also introduce a novel key-dependent chaotic variable generator to generate the required initial conditions and control parameters for the adopted enhanced chaotic map. The superiority of the proposed algorithm compared to some of the existing state-of-the-art has been verified through various performance metrics. These include different types of correlation coefficients, histogram visualization, histogram deviation, irregular deviation, mean square error, chi-square test, entropy test, and differential analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it