About: The Challenges of Rich Features in Universal Steganalysis     Goto   Sponge   Distinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • Contemporary steganalysis is driven by new steganographic rich feature sets, which consist of large numbers of weak features. Although extremely powerful when applied to supervised classification problems, they are not compatible with unsupervised universal steganalysis, because the unsupervised method cannot separate the signal (evidence of steganographic embedding) from the noise (cover content). This work tries to alleviate the problem, by means of feature extraction algorithms. We focus on linear projections informed by embedding methods, and propose a new method which we call calibrated least squares with the specific aim of making the projections sensitive to stego content yet insensitive to cover variation. Different projections are evaluated by their application to the anomaly detector from Ref. 1, and we are able to retain both the universality and the robustness of the method, while increasing its performance substantially.
  • Contemporary steganalysis is driven by new steganographic rich feature sets, which consist of large numbers of weak features. Although extremely powerful when applied to supervised classification problems, they are not compatible with unsupervised universal steganalysis, because the unsupervised method cannot separate the signal (evidence of steganographic embedding) from the noise (cover content). This work tries to alleviate the problem, by means of feature extraction algorithms. We focus on linear projections informed by embedding methods, and propose a new method which we call calibrated least squares with the specific aim of making the projections sensitive to stego content yet insensitive to cover variation. Different projections are evaluated by their application to the anomaly detector from Ref. 1, and we are able to retain both the universality and the robustness of the method, while increasing its performance substantially. (en)
Title
  • The Challenges of Rich Features in Universal Steganalysis
  • The Challenges of Rich Features in Universal Steganalysis (en)
skos:prefLabel
  • The Challenges of Rich Features in Universal Steganalysis
  • The Challenges of Rich Features in Universal Steganalysis (en)
skos:notation
  • RIV/68407700:21230/13:00210674!RIV14-GA0-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GPP103/12/P514)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 64887
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/13:00210674
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Steganalysis; novelty detection; feature extraction (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [898BAC23F16C]
http://linked.open...v/mistoKonaniAkce
  • San Francisco
http://linked.open...i/riv/mistoVydani
  • Washington
http://linked.open...i/riv/nazevZdroje
  • Media Watermarking, Security, and Forensics 2013
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Pevný, Tomáš
  • Ker, Andrew D.
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000329576200020
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1117/12.2006790
http://purl.org/ne...btex#hasPublisher
  • SPIE
https://schema.org/isbn
  • 9780819494382
http://localhost/t...ganizacniJednotka
  • 21230
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software