  Recapturing attack of document images is a topic with few research attention.
However, such attack can be employed as a simple but effective anti-forensic
tool for digital document images. In this work, we present a high quality
captured and recaptured image dataset of some representative identity documents
to facilitate the study of this important issue. To highlight the risks posed
by such attack, we evaluate some popular off-the-shelf machine learning-based
approaches with our database under different experimental protocols.
Experimental results reveal the risks of existing document recapture detection
algorithms under uncontrolled application scenarios.
