Copy-Move forgery has been widely studied as it is a really common forgery. Furthermore, it is the easiest forgery to create with serious security-related threats in particular for distant remote id onboarding where company ask their customer to send a photo of their ID document. It is then easy for a counterfeit to alter the information on the document by copying and pasting letters within the photo. On the other hand, copy-move detection algorithms are known to perform worse in presence of similar but genuine objects preventing us from using them in practical situations like remote ID on boarding. In this article we propose a novel copy-move public dataset containing forged ID documents and study current state-of-the-art performances on this dataset to evaluate their potential use in practical situations.