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Image recapture seriously breaks the fairness of artificial intelligent (AI)systems, which deceives the system by recapturing others' images. Most of theexisting recapture models can only address a single pattern of recapture (e.g.,moire, edge, artifact, and others) based on the datasets with simulatedrecaptured images using fixed electronic devices. In this paper, we explicitlyredefine image recapture forensic task as four patterns of image recapturerecognition, i.e., moire recapture, edge recapture, artifact recapture, andother recapture. Meanwhile, we propose a novel Feature Disentanglement andDynamic Fusion (FDDF) model to adaptively learn the most effective recapturefeature 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 thenumber of previously published datasets. To the best of our knowledge, we arethe first to propose a general model and a general real-scene large-scaledataset for recaptured image forensic. Extensive experiments show that ourproposed FDDF can achieve state-of-the-art performance on the RUR dataset.

Learning Feature Disentanglement and Dynamic Fusion for Recaptured Image Forensic