Matches in SemOpenAlex for { <https://semopenalex.org/work/W1964317637> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W1964317637 endingPage "063020" @default.
- W1964317637 startingPage "063020" @default.
- W1964317637 abstract "Image steganography delivers secret data by slight modifications of the cover. To detect these data, steganalysis tries to create some features to embody the discrepancy between the cover and steganographic images. Therefore, the urgent problem is how to design an effective classification architecture for given feature vectors extracted from the images. We propose an approach to automatically select effective features based on the well-known JPEG steganographic methods. This approach, referred to as extreme learning machine revisited feature selection (ELM-RFS), can tune input weights in terms of the importance of input features. This idea is derived from cross-validation learning and one-dimensional (1-D) search. While updating input weights, we seek the energy decreasing direction using the leave-one-out (LOO) selection. Furthermore, we optimize the 1-D energy function instead of directly discarding the least significant feature. Since recent Liu features can gain considerable low detection errors compared to a previous JPEG steganalysis, the experimental results demonstrate that the new approach results in less classification error than other classifiers such as SVM, Kodovsky ensemble classifier, direct ELM-LOO learning, kernel ELM, and conventional ELM in Liu features. Furthermore, ELM-RFS achieves a similar performance with a deep Boltzmann machine using less training time." @default.
- W1964317637 created "2016-06-24" @default.
- W1964317637 creator A5005011787 @default.
- W1964317637 creator A5043596876 @default.
- W1964317637 creator A5071724015 @default.
- W1964317637 date "2014-12-17" @default.
- W1964317637 modified "2023-09-23" @default.
- W1964317637 title "Effective feature selection for image steganalysis using extreme learning machine" @default.
- W1964317637 cites W1998058925 @default.
- W1964317637 cites W2026131661 @default.
- W1964317637 cites W2050297026 @default.
- W1964317637 cites W2095635024 @default.
- W1964317637 cites W2100495367 @default.
- W1964317637 cites W2111072639 @default.
- W1964317637 cites W2116166670 @default.
- W1964317637 cites W2119821739 @default.
- W1964317637 cites W2124664712 @default.
- W1964317637 doi "https://doi.org/10.1117/1.jei.23.6.063020" @default.
- W1964317637 hasPublicationYear "2014" @default.
- W1964317637 type Work @default.
- W1964317637 sameAs 1964317637 @default.
- W1964317637 citedByCount "1" @default.
- W1964317637 countsByYear W19643176372019 @default.
- W1964317637 crossrefType "journal-article" @default.
- W1964317637 hasAuthorship W1964317637A5005011787 @default.
- W1964317637 hasAuthorship W1964317637A5043596876 @default.
- W1964317637 hasAuthorship W1964317637A5071724015 @default.
- W1964317637 hasConcept C107368093 @default.
- W1964317637 hasConcept C108801101 @default.
- W1964317637 hasConcept C115961682 @default.
- W1964317637 hasConcept C119857082 @default.
- W1964317637 hasConcept C12267149 @default.
- W1964317637 hasConcept C138885662 @default.
- W1964317637 hasConcept C148483581 @default.
- W1964317637 hasConcept C153180895 @default.
- W1964317637 hasConcept C154945302 @default.
- W1964317637 hasConcept C198751489 @default.
- W1964317637 hasConcept C2776401178 @default.
- W1964317637 hasConcept C2780150128 @default.
- W1964317637 hasConcept C41008148 @default.
- W1964317637 hasConcept C41895202 @default.
- W1964317637 hasConcept C50644808 @default.
- W1964317637 hasConcept C52622490 @default.
- W1964317637 hasConcept C95623464 @default.
- W1964317637 hasConceptScore W1964317637C107368093 @default.
- W1964317637 hasConceptScore W1964317637C108801101 @default.
- W1964317637 hasConceptScore W1964317637C115961682 @default.
- W1964317637 hasConceptScore W1964317637C119857082 @default.
- W1964317637 hasConceptScore W1964317637C12267149 @default.
- W1964317637 hasConceptScore W1964317637C138885662 @default.
- W1964317637 hasConceptScore W1964317637C148483581 @default.
- W1964317637 hasConceptScore W1964317637C153180895 @default.
- W1964317637 hasConceptScore W1964317637C154945302 @default.
- W1964317637 hasConceptScore W1964317637C198751489 @default.
- W1964317637 hasConceptScore W1964317637C2776401178 @default.
- W1964317637 hasConceptScore W1964317637C2780150128 @default.
- W1964317637 hasConceptScore W1964317637C41008148 @default.
- W1964317637 hasConceptScore W1964317637C41895202 @default.
- W1964317637 hasConceptScore W1964317637C50644808 @default.
- W1964317637 hasConceptScore W1964317637C52622490 @default.
- W1964317637 hasConceptScore W1964317637C95623464 @default.
- W1964317637 hasIssue "6" @default.
- W1964317637 hasLocation W19643176371 @default.
- W1964317637 hasOpenAccess W1964317637 @default.
- W1964317637 hasPrimaryLocation W19643176371 @default.
- W1964317637 hasRelatedWork W1968515670 @default.
- W1964317637 hasRelatedWork W2055625960 @default.
- W1964317637 hasRelatedWork W2103229133 @default.
- W1964317637 hasRelatedWork W2200767093 @default.
- W1964317637 hasRelatedWork W2327530886 @default.
- W1964317637 hasRelatedWork W2461139954 @default.
- W1964317637 hasRelatedWork W2579804281 @default.
- W1964317637 hasRelatedWork W2959352717 @default.
- W1964317637 hasRelatedWork W2982078027 @default.
- W1964317637 hasRelatedWork W2345184372 @default.
- W1964317637 hasVolume "23" @default.
- W1964317637 isParatext "false" @default.
- W1964317637 isRetracted "false" @default.
- W1964317637 magId "1964317637" @default.
- W1964317637 workType "article" @default.