Abstract: In this paper, we propose novel algorithmic models
based on information fusion and feature transformation in crossmodal
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.