Svd-based Tamper Proofing of Multi-attribute Motion Data

Repositories of motion captured (MoCap) data can be reused for human motion analysis in physical medicine, biomechanics and animation related entertainment industry. MoCap data expressed as a matrix Mm x n can be subject to tampering from shuffling of its elements or change in element values due to motion editing operations. Tampering of archived motion data intentionally or due to machine/human errors, may result in loss of research, money and effort. The paper proposes singular value decomposition (SVD) based methodology for tamper proofing motion data. This tamper proofing methodology extracts reference patterns in the form of right and left singular vectors of motion data matrix M. These patterns are used to verify and trace the pattern of tampering. The use of first Eigen vectors for tamper detection reduces storage and computation complexities to O (m + n) and makes the solution scalable.