Identification of the motion estimation strategy using eigenalgorithms

The identification of the device, or device model, that was used to acquire a video sequence is a very challenging task, since it has to rely on subtle traces left by the processing steps applied to the raw acquired data. Previous works have tried to address this problem leveraging the traces left by the imaging sensor. However, in the case of video, lossy coding is often quite aggressive, thus making these methods impractical. In this work, we reverse the analysis strategy and exploit the traces left by lossy coding as telltale for the adopted acquisition device. Specifically, we aim at detecting the implementation of the video codec by identifying the adopted motion estimation algorithm. Indeed, motion estimation is not defined in video coding standards and, as such, it represents one of the non-normative tools that can be customized in the design of the encoder. The key tenet consists in studying the correlation between the motion vectors obtained from the decoded bitstream, and those computed using a set of known and diverse motion estimation algorithms, called eigenalgorithms. In our work, we generalize a method recently appeared in the literature, which assumes that the motion estimation algorithm used is necessarily one of those available during the analysis. Experimental results show that the approach is able to successfully identify the motion estimation algorithm in most cases