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Record W116021867

Geometrically robust image watermarking using star patterns

2007· article· en· W116021867 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 International Conference on Signal and Image Processing · 2007
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Steganography and Watermarking Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDigital watermarkingComputer scienceBitTorrent trackerThe InternetNoise (video)Image (mathematics)Computer visionPoint (geometry)Copy protectionDigital imageArtificial intelligenceDigital signatureDigital Watermarking AllianceImage processingComputer securityMathematicsWorld Wide WebEye tracking
DOInot available

Abstract

fetched live from OpenAlex

Digital copyright protection has become increasingly important in recent times due to the rapid growth of the Internet and the proliferation of P2P technologies. Copyright protection of digital images and videos are of particular interest since they can easily be pirated and distributed illegally across networks. Existing watermarking methods can embed a signature into a digital image and tend to be robust against signal noise and but less so against geometric transforms. Methods that are more robust against geometric manipulation are often implemented at great computational cost. This paper proposes a lower cost method using point patterns inspired in part by automated star trackers used in astronomy. The method is evaluated and presents some advantages over existing approaches. Weaknesses of the method and areas for future work are also discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.691
Threshold uncertainty score0.868

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 communication0.0010.002
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 head0.059
GPT teacher head0.316
Teacher spread0.258 · 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