ML Estimation of the Resampling Factor
In the last few years, an increasing number of passive forensic techniques have emerged with the aim of furnishing information about the authenticity, integrity or processing history of a multimedia content. In this paper, 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.