LAUSR

Nowadays, digital images are widely used in various services. The emergence of more and more image editing algorithms has made image forensic approaches to be severely challenged. Driven by the emergence of forged images, more and more image forensic methods are proposed to evaluate the authenticity of digital images. However, in some privacy-related image forensics areas, the scarcity of data affects their development. In this paper, we investigate a document image generation scheme based on face swapping and distortion generation to generate document image databases at a low cost. First, we propose an IDNet for editing face content and text content in digital document images. Second, we propose a Distortion Simulation Network (DSNet) for simulating print-and-scan distortion, and the generated data can be used to study a novel document attack type called recapture attack. Third, we use the database generated by our proposed method to assist in training document image recapture forensic networks. A qualitative comparison with existing methods illustrates that the image content generated by our proposed method maintains more complete semantic information and higher image quality. Quantitative results have confirmed that with the addition of the generated database, the performance of the model improves by about 8% measured in AUC.