In order to characterize errors of Digital Image Correlation (DIC) algorithms, sets of virtual images are often generated from a reference image by in-plane sub-pixel translations. This leads to the determination of the well-known S-shaped bias error curves and their corresponding random error curves. As images are usually shifted by using interpolation schemes similar to those used in DIC algorithms, the question of the possible bias in the quantification of measurement uncertainties of DIC softwares is raised and constitutes the main problematic of this paper. In this collaborative work, synthetic numerically shifted images are built from two methods: one based on interpolations of the reference image and the other based on the transformation of an analytic texture function. Images are analyzed using an in-house subset-based DIC software and results are compared and discussed. The effect of image noise is also highlighted. The main result is that the a priori choices to numerically shift the reference image modify DIC results and may lead to wrong conclusions in terms of DIC error assessment
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