  Image recapture seriously breaks the fairness of artificial intelligent (AI)
systems, which deceives the system by recapturing others' images. Most of the
existing recapture models can only address a single pattern of recapture (e.g.,
moire, edge, artifact, and others) based on the datasets with simulated
recaptured images using fixed electronic devices. In this paper, we explicitly
redefine image recapture forensic task as four patterns of image recapture
recognition, i.e., moire recapture, edge recapture, artifact recapture, and
other recapture. Meanwhile, we propose a novel Feature Disentanglement and
Dynamic Fusion (FDDF) model to adaptively learn the most effective recapture
feature representation for covering different recapture pattern recognition.
Furthermore, we collect a large-scale Real-scene Universal Recapture (RUR)
dataset containing various recapture patterns, which is about five times the
number of previously published datasets. To the best of our knowledge, we are
the first to propose a general model and a general real-scene large-scale
dataset for recaptured image forensic. Extensive experiments show that our
proposed FDDF can achieve state-of-the-art performance on the RUR dataset.
