Transmissão de Informação Embutida em Arquivos Comprimidos com o Algoritmo LZW
Bibliographic record
Abstract
Resumo-Este artigo apresenta um algoritmo para embutir dados adicionais em um arquivo comprimido através do codificador LZW.O método proposto utiliza o fato do codificador não remover completamente a redundância dos arquivos originais, para adicionar bits extras, sem qualquer perda de desempenho na compressão e sem criar qualquer distorc ¸ão nos dados originais.O desempenho do método é medido através da sua aplicac ¸ão em três grupos diferentes de arquivos: o conjunto de Calgary, o conjunto de Canterbury e um grupo de imagens naturais em tons de cinza.Os resultados de simulac ¸ão mostram que o algoritmo proposto consegue adicionar 0,1 bit a cada símbolo do arquivo original, na média. Palavras-Chave-informac ¸ão embutida, informac ¸ão camuflada, esteganografia, compressão de dadosAbstract-This paper presents an algorithm to embed additional data in a LZW compressed file.The proposed method makes use the fact that LZW does not completely remove all redundancy to add extra bits in the codeword, with no penalty in compression rate and no distortion in original data.The performance of the method is measured using three different groups of files: Calgary Corpus, Canterbury Corpus, and a group of natural graylevel images.Simulation results have shown that the algorithm can add 0.1 bit per symbol of the original data, in the mean.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".