Blind image tamper detection based on multimodal fusion

Girija Chetty, Monica Singh, Matthew White

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

In this paper, we propose a novel feature processing approach based on fusion of noise and quantization residue features for detecting tampering or forgery in video sequences. The evaluation of proposed residue features – the noise residue features and the quantization features, their transformation in optimal feature subspace based on fisher linear discriminant features and canonical correlation analysis features, and their subsequent fusion for emulated copy-move tamper scenarios shows a significant improvement in tamper detection accuracy.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science: Neural Information Processing: Models and Application: 17th International Conference, ICONIP 2010 Sydney, Australia, November 22-25, 2010 Proceedings Part II
EditorsKok Wai Wong, B. Sumudu U Mendis, Abdesselam Bouzerdoum
Place of PublicationBerlin, Germany
PublisherSpringer
Pages557-564
Number of pages8
DOIs
Publication statusPublished - 2010
EventICONIP 2010 - 17th International Conference on Neural Information Processing - Sydney, Australia
Duration: 22 Nov 201025 Nov 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume6444

Conference

ConferenceICONIP 2010 - 17th International Conference on Neural Information Processing
CountryAustralia
CitySydney
Period22/11/1025/11/10

Fingerprint Dive into the research topics of 'Blind image tamper detection based on multimodal fusion'. Together they form a unique fingerprint.

  • Cite this

    Chetty, G., Singh, M., & White, M. (2010). Blind image tamper detection based on multimodal fusion. In K. W. Wong, B. S. U. Mendis, & A. Bouzerdoum (Eds.), Lecture Notes in Computer Science: Neural Information Processing: Models and Application: 17th International Conference, ICONIP 2010 Sydney, Australia, November 22-25, 2010 Proceedings Part II (pp. 557-564). (Lecture Notes in Computer Science ; Vol. 6444). Springer. https://doi.org/10.1007/978-3-642-17534-3_69