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Record W2379013923

On Encryption Algorithm of Color Image Based on Single Channel RGB Components

2014· article· en· W2379013923 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

VenueJournal of Southwest China Normal University · 2014
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsThe Alberta Paraplegic Foundation
Fundersnot available
KeywordsEncryptionRGB color modelColor imageAlgorithmDiscrete cosine transformComputer scienceMathematicsChannel (broadcasting)Image (mathematics)Computer visionArtificial intelligenceImage processing
DOInot available

Abstract

fetched live from OpenAlex

In order to ensure the safety and reliability of image transmission,a novel encryption of color image algorithm based on single channel RGB components has been studies in this paper,to solve the problem which traditional multi-channel algorithms cannot synchronous encryption and large load.Firstly,RGB components of color image have been extracted,plural matrix obtained by the discrete cosine transform and ZigZag,and complex matrix scrambled by the logistic chaos;secondly,inverse discrete cosine transform and chaos masking have been used to reconstruction and encryption RGB component to obtain crypt image;finally,the performance has been tested by simulation experiments.The results show that,compared with other color image encryption algorithms,the proposed algorithm has some good characteristics such as good confusion and diffusion properties,decryption speed,can resist various attacks and can effectively guarantee the security of image encryption,so it has a certain practical value.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.583
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.182
Teacher spread0.174 · 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