On Physically Occluded Fake Identity Document Detection

Many online applications require the users to upload their identity documents for authentication. The fake identity document is one of the main threats which compromises the security and reliability of such online applications. Existing techniques focus on the detection of digitally forged identity documents, which neglect the impact of physical forgeries. In this paper, we look into the problem of detecting physically occluded fake identity documents, which can be easily generated without any image processing knowledge. We observe that the physical occlusions inevitably produce occluded boundaries on the document. To take the advantage, we propose an Occluded Boundary Representation Learning (OBRL) module to progressively learn the occluded boundary features. These are then fed into an Occluded Boundary Message Passing (OBMP) module to effectively diffuse the physical occlusion traces to enhance the backbone features for robust detection. We newly construct a Physically Occluded Fake ID Card image dataset (POID) for evaluation. Various experiments are conducted on the POID, where our scheme is able to achieve 99.6% of accuracy in detecting physically occluded fake ID card images with a mAP of over 85% to localize the occlusion regions.