  Recapture detection of face and document images is an important forensic
task. With deep learning, the performances of face anti-spoofing (FAS) and
recaptured document detection have been improved significantly. However, the
performances are not yet satisfactory on samples with weak forensic cues. The
amount of forensic cues can be quantified to allow a reliable forensic result.
In this work, we propose a forensicability assessment network to quantify the
forensicability of the questioned samples. The low-forensicability samples are
rejected before the actual recapturing detection process to improve the
efficiency of recapturing detection systems. We first extract forensicability
features related to both image quality assessment and forensic tasks. By
exploiting domain knowledge of the forensic application in image quality and
forensic features, we define three task-specific forensicability classes and
the initialized locations in the feature space. Based on the extracted features
and the defined centers, we train the proposed forensic assessment network
(FANet) with cross-entropy loss and update the centers with a momentum-based
update method. We integrate the trained FANet with practical recapturing
detection schemes in face anti-spoofing and recaptured document detection
tasks. Experimental results show that, for a generic CNN-based FAS scheme,
FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by
rejecting samples with the lowest 30% forensicability scores. The performance
of FAS schemes is poor in the rejected samples, with EER as high as 56.48%.
Similar performances in rejecting low-forensicability samples have been
observed for the state-of-the-art approaches in FAS and recaptured document
detection tasks. To the best of our knowledge, this is the first work that
assesses the forensicability of recaptured document images and improves the
system efficiency.
