MétaCan
Menu
Back to cohort
Record W2022123767 · doi:10.1155/2013/848392

An Improved Secure Image Encryption Algorithm Based on Rubik's Cube Principle and Digital Chaotic Cipher

2013· article· en· W2022123767 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

VenueMathematical Problems in Engineering · 2013
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEncryptionCipherCube (algebra)AlgorithmCryptosystemImage (mathematics)Bitwise operationKey spaceCHAOS (operating system)Computer scienceBlock cipherWatermarking attackDigital imageCryptographyChaoticMathematicsTheoretical computer scienceProbabilistic encryptionDeterministic encryptionImage processingArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

A recently proposed secure image encryption scheme has drawn attention to the limited security offered by chaos-based image encryption schemes (mainly due to their relatively small key space) proposing a highly robust approach, based on Rubik's cube principle. This paper aims to study a newly designed image cryptosystem that uses the Rubik's cube principle in conjunction with a digital chaotic cipher. Thus, the original image is shuffled on Rubik's cube principle (due to its proven confusion properties), and then XOR operator is applied to rows and columns of the scrambled image using a chaos-based cipher (due to its proven diffusion properties). Finally, the experimental results and security analysis show that the newly proposed image encryption scheme not only can achieve good encryption and perfect hiding ability but also can resist any cryptanalytic attacks (e.g., exhaustive attack, differential attack, statistical attack, etc.).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.747
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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