  Recapturing attack can be employed as a simple but effective anti-forensic
tool for digital document images. Inspired by the document inspection process
that compares a questioned document against a reference sample, we proposed a
document recapture detection scheme by employing Siamese network to compare and
extract distinct features in a recapture document image. The proposed algorithm
takes advantages of both metric learning and image forensic techniques. Instead
of adopting Euclidean distance-based loss function, we integrate the forensic
similarity function with a triplet loss and a normalized softmax loss. After
training with the proposed triplet selection strategy, the resulting feature
embedding clusters the genuine samples near the reference while pushes the
recaptured samples apart. In the experiment, we consider practical domain
generalization problems, such as the variations in printing/imaging devices,
substrates, recapturing channels, and document types. To evaluate the
robustness of different approaches, we benchmark some popular off-the-shelf
machine learning-based approaches, a state-of-the-art document image detection
scheme, and the proposed schemes with different network backbones under various
experimental protocols. Experimental results show that the proposed schemes
with different network backbones have consistently outperformed the
state-of-the-art approaches under different experimental settings.
Specifically, under the most challenging scenario in our experiment, i.e.,
evaluation across different types of documents that produced by different
devices, we have achieved less than 5.00% APCER (Attack Presentation
Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation
Classification Error Rate) by the proposed network with ResNeXt101 backbone at
5% BPCER decision threshold.
