Blind image tamper detection based on multimodal fusion

Authors:
Girija Chetty;Monica Singh;Matthew White
Affiliations:
Faculty of Information Sciences and Engineering, University of Canberra, Australia;Video Analytics Pty. Ltd. Melbourne, Australia;Video Analytics Pty. Ltd. Melbourne, Australia
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

Citing 7
Cited 0

PCA versus LDA

IEEE Transactions on Pattern Analysis and Machine Intelligence
A natural image model approach to splicing detection

Proceedings of the 9th workshop on Multimedia & security
Robust face-voice based speaker identity verification using multilevel fusion

Image and Vision Computing
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

Quantified Score

Hi-index	0.06

Visualization

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.