MétaCan
Menu
Back to cohort
Record W4280517299 · doi:10.3389/fphy.2022.911156

A Chaos-Based Image Encryption Scheme Using the Hamming Distance and DNA Sequence Operation

2022· article· en· W4280517299 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Physics · 2022
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEncryptionHamming distanceScramblingChaoticPermutation (music)AlgorithmCHAOS (operating system)MathematicsSequence (biology)Substitution (logic)CryptographyComputer scienceTheoretical computer scienceArtificial intelligencePhysicsGeneticsBiologyComputer networkComputer security

Abstract

fetched live from OpenAlex

In this study, we introduced a new memristive chaotic system with the rich dynamic behavior, and then we proposed a chaotic-based image encryption scheme which is based on the permutation–confusion–substitution structure. In our scheme, the Hamming distance is used to design a plain-related chaotic system initial condition, and the generated chaotic sequences are assigned to permutation, diffusion, and substitution stages. In the permutation stage, an effect pixel confusion is implemented through a new permutation approach, which is a double-ended select-swap scrambling strategy. In the diffusion stage, DNA XOR operation is implemented followed by DNA triploid mutation which is introduced to enhance the strength of our encryption system. A number of experiments and extensive safety analysis have been carried out and the results fully justify that our scheme not only ensures desirable security but also has superior efficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.251
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it