Derivation of the Asymptotic Eigenvalue Distribution for Causal 2D-AR Models under Upscaling
This work addresses the problem of image forensics by enabling detection and estimation of resampling in images, though it appears incremental as it builds on existing 2D-AR models.
The paper derived the asymptotic eigenvalue distribution for causal 2D-AR models under upscaling, providing analytical results for sample autocorrelation matrices of genuine and upscaled images, and included pseudocode for resampling detection and factor estimation.
This technical report describes the derivation of the asymptotic eigenvalue distribution for causal 2D-AR models under an upscaling scenario. Specifically, it tackles the analytical derivation of the asymptotic eigenvalue distribution of the sample autocorrelation matrix corresponding to genuine and upscaled images. It also includes the pseudocode of the derived approaches for resampling detection and resampling factor estimation that are based on this analysis.