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Record W2151686053 · doi:10.1109/tcsvt.2003.815959

RST-invariant digital image watermarking based on log-polar mapping and phase correlation

2003· article· en· W2151686053 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

VenueIEEE Transactions on Circuits and Systems for Video Technology · 2003
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Steganography and Watermarking Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWatermarkInvariant (physics)MathematicsDigital watermarkingPhase correlationScalingTranslation (biology)Artificial intelligenceComputer visionAlgorithmFourier transformImage (mathematics)Computer scienceFourier analysisMathematical analysisGeometryFractional Fourier transform

Abstract

fetched live from OpenAlex

Based on log-polar mapping (LPM) and phase correlation, the paper presents a novel digital image watermarking scheme that is invariant to rotation, scaling, and translation (RST). We embed a watermark in the LPMs of the Fourier magnitude spectrum of an original image, and use the phase correlation between the LPM of the original image and the LPM of the watermarked image to calculate the displacement of watermark positions in the LPM domain. The scheme preserves the image quality by avoiding computing the inverse log-polar mapping (ILPM), and produces smaller correlation coefficients for unwatermarked images by using phase correlation to avoid exhaustive search. The evaluations demonstrate that the scheme is invariant to rotation and translation, invariant to scaling when the scale is in a reasonable range, and very robust to JPEG compression.

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: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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