Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10609/146477
Registro completo de metadatos
Campo DC Valor Lengua/Idioma
dc.contributor.authorKheddar, Hamza-
dc.contributor.authorMegias, David-
dc.contributor.otherUniversity of Medea-
dc.contributor.otherUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)-
dc.contributor.otherCYBERCAT - Center for Cybersecurity Research of Catalonia-
dc.date.accessioned2022-07-11T14:08:21Z-
dc.date.available2022-07-11T14:08:21Z-
dc.date.issued2022-01-06-
dc.identifier.citationKheddar, H., & Megías, D. (2022). High capacity speech steganography for the G723.1 coder based on quantised line spectral pairs interpolation and CNN auto-encoding. Applied Intelligence, 52(8), 9441-9459. doi: 10.1007/s10489-021-02938-7-
dc.identifier.issn0924-669XMIAR
-
dc.identifier.urihttp://hdl.handle.net/10609/146477-
dc.description.abstractIn this paper, a novel steganographic method for Voice over IP applications -called Steganography-based Interpolation and Auto-Encoding (SIAE)- is proposed. The aim of the proposed scheme is to securely transmit a secret speech hidden within another (cover) speech coded with a G723.1 coder. SIAE embeds the steganograms in four interpolated and quantised line spectral pairs (QLSP) vectors. In order to minimize the changes in the cover speech, the proposed approach uses a 1D auto-encoder to compress the payload, and this scheme only requires embedding eight bits in about 30% of the packets. At the receiver side, the secret data can be successfully expanded to its original size upon decoding. This represents a significant reduction in the number of modified bits compared to state-of-the-art schemes, and results in enhanced undetectability and decreased steganographic quality loss. The results show that the proposed auto-encoder scheme has a very high performance since it can compress the embedded data up to 80 times from its original size, leading to a steganographic capacity that exceeds one kilobit per second (kpbs). In terms of imperceptibility, which is a relevant property for speech-in-speech steganography, the proposed SIAE method entails a very imperceptible distortion, with an average steganographic quality loss not greater than 0.19 in terms of mean opinion scores (MOS). Last but not least, the proposed method evades steganalysis specifically targeted at speech steganography. The tested steganalytic methods fail in detecting the steganographic content produced with the proposed SIAE method, yielding classification results that are indistinguishable from random guessing.en
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherApplied Intelligence-
dc.relation.ispartofApplied Intelligence, 2022, 52-
dc.relation.ispartofseries52;-
dc.rightsCC BY-NC-ND 4.0-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.source.urihttps://doi.org/10.1007/s10489-021-02938-7-
dc.subjectauto-encoderen
dc.subjectconvolutional neural networksen
dc.subjectmulti-pulse maximum likelihood quantisationen
dc.subjectG.723.1en
dc.subjectspeech steganographyen
dc.subjectinterpolationen
dc.subjectcodificador automàticca
dc.subjectxarxes neuronals convolucionalsca
dc.subjectquantificació de màxima probabilitat multipolsca
dc.subjectesteganografia de la parlaca
dc.subjectinterpolacióca
dc.subjectcodificador automáticoes
dc.subjectredes neuronales convolucionaleses
dc.subjectcuantificación de máxima verosimilitud multipulsoes
dc.subjectesteganografía del hablaes
dc.subjectinterpolación-
dc.subject.lcshneural networks (Computer science)en
dc.titleHigh capacity speech steganography for the G723.1 coder based on quantised line spectral pairs interpolation and CNN auto-encoding-
dc.typeinfo:eu-repo/semantics/article-
dc.subject.lemacxarxes neuronals (Informàtica)ca
dc.subject.lcshesredes neuronales artificialeses
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccess-
dc.identifier.doihttp://doi.org/10.1007/s10489-021-02938-7-
dc.gir.idAR/0000009361-
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/RTI2018-095094-B-C22-
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/PCI2020-120689-2-
dc.type.versioninfo:eu-repo/semantics/acceptedVersion-
dc.date.embargoEndDate2023-01-07-
Aparece en las colecciones: Articles cientÍfics
Articles

Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
megias_ai_high.pdf1,04 MBAdobe PDFVista previa
Visualizar/Abrir