Blind Video Tamper Detection Based on Fusion of Source Features

Julian Goodwin; Girija Chetty

doi:10.1109/DICTA.2011.108

Blind Video Tamper Detection Based on Fusion of Source Features

Julian Goodwin, Girija Chetty

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

22 Citations (Scopus)

Abstract

In this paper, we propose novel algorithmic models based on information fusion and feature transformation in cross-modal subspace for different types of residue features extracted from several intra-frame and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features - the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.

Original language	English
Title of host publication	Proceedings Digital Image Computing Techniques and Applications 2011
Editors	Andrew Bradley, Paul Jackaway, Murk Bottema
Place of Publication	New Jersey
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	608-613
Number of pages	6
ISBN (Electronic)	9780769545882
ISBN (Print)	9781457720062
DOIs	https://doi.org/10.1109/DICTA.2011.108
Publication status	Published - 6 Dec 2011
Event	2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011 - Noosa, Noosa, Australia Duration: 6 Dec 2011 → 8 Dec 2011

Conference

Conference	2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011
Country/Territory	Australia
City	Noosa
Period	6/12/11 → 8/12/11

Access to Document

10.1109/DICTA.2011.108

Cite this

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title = "Blind Video Tamper Detection Based on Fusion of Source Features",

abstract = "In this paper, we propose novel algorithmic models based on information fusion and feature transformation in cross-modal subspace for different types of residue features extracted from several intra-frame and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features - the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.",

keywords = "tamper detection, image forensics, correlation features",

author = "Julian Goodwin and Girija Chetty",

year = "2011",

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doi = "10.1109/DICTA.2011.108",

language = "English",

isbn = "9781457720062",

pages = "608--613",

editor = "Andrew Bradley and Paul Jackaway and Murk Bottema",

booktitle = "Proceedings Digital Image Computing Techniques and Applications 2011",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011 ; Conference date: 06-12-2011 Through 08-12-2011",

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Goodwin, J & Chetty, G 2011, Blind Video Tamper Detection Based on Fusion of Source Features. in A Bradley, P Jackaway & M Bottema (eds), Proceedings Digital Image Computing Techniques and Applications 2011. IEEE, Institute of Electrical and Electronics Engineers, New Jersey, pp. 608-613, 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011, Noosa, Queensland, Australia, 6/12/11. https://doi.org/10.1109/DICTA.2011.108

Blind Video Tamper Detection Based on Fusion of Source Features. / Goodwin, Julian; Chetty, Girija.
Proceedings Digital Image Computing Techniques and Applications 2011. ed. / Andrew Bradley; Paul Jackaway; Murk Bottema. New Jersey: IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 608-613.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Blind Video Tamper Detection Based on Fusion of Source Features

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AU - Chetty, Girija

PY - 2011/12/6

Y1 - 2011/12/6

N2 - In this paper, we propose novel algorithmic models based on information fusion and feature transformation in cross-modal subspace for different types of residue features extracted from several intra-frame and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features - the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.

AB - In this paper, we propose novel algorithmic models based on information fusion and feature transformation in cross-modal subspace for different types of residue features extracted from several intra-frame and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features - the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.

KW - tamper detection

KW - image forensics

KW - correlation features

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DO - 10.1109/DICTA.2011.108

M3 - Conference contribution

SN - 9781457720062

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EP - 613

BT - Proceedings Digital Image Computing Techniques and Applications 2011

A2 - Bradley, Andrew

A2 - Jackaway, Paul

A2 - Bottema, Murk

PB - IEEE, Institute of Electrical and Electronics Engineers

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T2 - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011

Y2 - 6 December 2011 through 8 December 2011

ER -

Blind Video Tamper Detection Based on Fusion of Source Features

Abstract

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