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Record W2012243263 · doi:10.1155/asp/2006/86712

Time-Frequency Signal Synthesis and Its Application in Multimedia Watermark Detection

2006· article· en· W2012243263 on OpenAlex
Lam Le, Sridhar Krishnan

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

VenueEURASIP Journal on Advances in Signal Processing · 2006
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Steganography and Watermarking Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWatermarkChirpComputer scienceDigital watermarkingTransmitterSIGNAL (programming language)Noise (video)Image (mathematics)Artificial intelligenceComputer visionSpeech recognitionTelecommunications

Abstract

fetched live from OpenAlex

We propose a novel approach to detect the watermark message embedded in images under the form of a linear frequency modulated chirp. Localization of several time-frequency distributions (TFDs) is studied for different frequency modulated signals under various noise conditions. Smoothed pseudo-Wigner-Ville distribution (SPWVD) is chosen and applied to detect and recover the corrupted image watermark bits at the receiver. The synthesized watermark message is compared with the referenced one at the transmitter as a detection evaluation scheme. The correlation coefficient between the synthesized and the referenced chirps reaches 0.9 or above for a maximum bit error rate of 15% under intentional and nonintentional attacks. The method provides satisfactory result for detection of image watermark messages modulated as chirp signal and could be a potential tool in multimedia security applications.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
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 head0.249
Teacher spread0.241 · 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