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Record W2203669009 · doi:10.1109/have.2015.7359451

Adaptive digital watermarking using RDWT and SVD

2015· article· en· W2203669009 on OpenAlexaff
Lei Chen, Jiying Zhao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Steganography and Watermarking Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDigital watermarkingWatermarkSingular value decompositionComputer scienceArtificial intelligenceComputer visionDiscrete wavelet transformVisibilityNoise (video)WaveletEmbeddingImage (mathematics)Wavelet transform

Abstract

fetched live from OpenAlex

This paper proposes a new scheme for secure digital watermarking and copyright protection of videos. The approach presented here is based on redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD). The video frames and the watermark are decomposed by RDWT, and the watermark is embedded into the subbands of the I-frames. The singular values of the I-frames are modified with the singular values of the visual watermark. Meanwhile, the watermark embedding strength is adjusted adaptively using the noise visibility function (NVF) with local properties. The approach is efficient and appropriate for real-time applications. The experimental results demonstrate that the proposed approach has good performance in preserving high quality of the video, and the watermark can be extracted accurately after various attacks.

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.

How this classification was reachedexpand

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score0.334

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 communication0.0000.001
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 head0.051
GPT teacher head0.263
Teacher spread0.211 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2015
Admission routes1
Has abstractyes

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