Multi-modal Document Presentation Attack Detection With Forensics Trace
  Disentanglement
Changsheng Chen Yongyi Deng Liangwei Lin Zitong Yu Zhimao Lai
Document Presentation Attack Detection (DPAD) is an important measure in
protecting the authenticity of a document image. However, recent DPAD methods
demand additional resources, such as manual effort in collecting additional
data or knowing the parameters of acquisition devices. This work proposes a
DPAD method based on multi-modal disentangled traces (MMDT) without the above
drawbacks. We first disentangle the recaptured traces by a self-supervised
disentanglement and synthesis network to enhance the generalization capacity in
document images with different contents and layouts. Then, unlike the existing
DPAD approaches that rely only on data in the RGB domain, we propose to
explicitly employ the disentangled recaptured traces as new modalities in the
transformer backbone through adaptive multi-modal adapters to fuse RGB/trace
features efficiently. Visualization of the disentangled traces confirms the
effectiveness of the proposed method in different document contents. Extensive
experiments on three benchmark datasets demonstrate the superiority of our MMDT
method on representing forensic traces of recapturing distortion.