  With the ongoing popularization of online services, the digital document
images have been used in various applications. Meanwhile, there have emerged
some deep learning-based text editing algorithms which alter the textual
information of an image . In this work, we present a document forgery algorithm
to edit practical document images. To achieve this goal, the limitations of
existing text editing algorithms towards complicated characters and complex
background are addressed by a set of network design strategies. First, the
unnecessary confusion in the supervision data is avoided by disentangling the
textual and background information in the source images. Second, to capture the
structure of some complicated components, the text skeleton is provided as
auxiliary information and the continuity in texture is considered explicitly in
the loss function. Third, the forgery traces induced by the text editing
operation are mitigated by some post-processing operations which consider the
distortions from the print-and-scan channel. Quantitative comparisons of the
proposed method and the exiting approach have shown the advantages of our
design by reducing the about 2/3 reconstruction error measured in MSE,
improving reconstruction quality measured in PSNR and in SSIM by 4 dB and 0.21,
respectively. Qualitative experiments have confirmed that the reconstruction
results of the proposed method are visually better than the existing approach.
More importantly, we have demonstrated the performance of the proposed document
forgery algorithm under a practical scenario where an attacker is able to alter
the textual information in an identity document using only one sample in the
target domain. The forged-and-recaptured samples created by the proposed text
editing attack and recapturing operation have successfully fooled some existing
document authentication systems.
