ML estimation of the resampling factor

Abstract

In this work, the problem of resampling factor estimation for tampering detection is addressed following the maximum likelihood criterion. By relying on the rounding operation applied after resampling, an approximation of the likelihood function of the quantized resampled signal is obtained. From the underlying statistical model, the maximum likelihood estimate is derived for one-dimensional signals and a piecewise linear interpolation. The performance of the obtained estimator is evaluated, showing that it outperforms state-of-the-art methods.