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Record W2510139783 · doi:10.1109/antem.2016.7550111

Information leakage detection from computer display in electromagnetic radiation

2016· article· en· W2510139783 on OpenAlex
Jun Shi, Degang Sun, Xuejie Ding, Jianlin Hu, Abbas Yongaçoğlu

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
FieldComputer Science
TopicDigital Media Forensic Detection
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInformation leakageComputer scienceRadiationLeakage (economics)Information theoryComputer visionElectromagnetic radiationArtificial intelligenceSet (abstract data type)Probability distributionOpticsPhysicsMathematicsStatisticsComputer security

Abstract

fetched live from OpenAlex

Information might be leaked through electromagnetic radiation from a computer display. Radiation containing information and radiation without information should be treated differently. Therefore, a novel algorithm has been developed to determine whether or not information is leaked in electromagnetic radiation. Motivated by the observation that the Gabor coefficients of information part and the non-information part of reconstructed image follow different statistical distribution, we propose a method to describe these differences and detect information leakage. In this method, probability graph theory is used and the probability mean distance is defined. In addition, the detection rule is set to realize automatic detection of information. By using this method, we can accurately and efficiently detect the information leakage in electromagnetic radiation from a computer display.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.592

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 communication0.0000.004
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 head0.003
GPT teacher head0.170
Teacher spread0.168 · 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

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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