Blind and passive digital video tamper detection based on multimodal fusion

Authors:
Girija Chetty
Affiliations:
Faculty of Information Sciences and Engineering, University of Canberra, Australia
Venue:
ICCOM'10 Proceedings of the 14th WSEAS international conference on Communications
Year:
2010

Citing 8
Cited 1

Fast features for face authentication under illumination direction changes

Pattern Recognition Letters
Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
A natural image model approach to splicing detection

Proceedings of the 9th workshop on Multimedia & security
Skipping spare information in multimodal inputs during multimodal input fusion

Proceedings of the 14th international conference on Intelligent user interfaces
An efficient and robust method for detecting copy-move forgery

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Image tamper detection based on demosaicing artifacts

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Exposing digital forgeries by detecting traces of resampling

IEEE Transactions on Signal Processing
Region filling and object removal by exemplar-based image inpainting

IEEE Transactions on Image Processing

Detecting removed object from video with stationary background

IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose novel algorithmic models based on feature transformation in cross-modal subspace and their multimodal fusion for different types of residue features extracted from several intraframe 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.