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Record W2094555630 · doi:10.1109/ism.2012.60

2D-FRFT Based Rotation Invariant Digital Image Watermarking

2012· article· en· W2094555630 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

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicMathematical Analysis and Transform Methods
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInvariant (physics)Fractional Fourier transformDigital watermarkingArtificial intelligenceRobustness (evolution)Computer scienceComputer visionChirpPattern recognition (psychology)Fourier transformAlgorithmMathematicsImage (mathematics)Fourier analysisMathematical analysisPhysicsOptics

Abstract

fetched live from OpenAlex

The extraction of rotation invariant representation is important for many signal processing problems such as image analysis, computer vision, and pattern recognition. In this paper, we present a systematic analysis of the Two-Dimensional Fractional Fourier Transform (2D-FRFT), and show that under certain conditions, the 2D-FRFT technique possesses the attractive property of rotation invariance. Based on our analysis, we proposed a novel digital image watermarking method which combines 2D chirp signal with the addition and rotation invariant properties of 2D-FRFT to achieve improved robustness and security. The effectiveness of the proposed solution is demonstrated through experiments.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.336
Teacher spread0.277 · 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