A distortion model-based pre-screening method for document image tampering localization under recapturing attack

Document images are vulnerable to tampering by image editing tools. The forgery trace can be concealed by a simple but effective counter-forensic measure, i.e., recapturing the altered document image. It is of practical need to study the tampering localization method under recapturing attack. In this work, we first study spatial and spectral distortion models in the printing and scanning process. The distortion models are then employed in extracting spectral features in both tampered and untampered regions. The proposed forensic scheme can then be established by comparing the spectral features in both the questioned document image and the reference halftone patterns (obtained by exploiting the prior knowledge of the printing device). To evaluate the performance of our approach, we gather a high-quality image database of 528 captured or recaptured documents (about 185K patches) as well as 72 tampered-and-recaptured documents (about 27K patches). The experimental results show that the proposed method can accurately classify recaptured document images with AUC as high as 0.9999 even though the training and testing samples are collected by different devices. In the tampering localization experiment, the proposed method can be combined with some generic CNN models to yield a two-stage scheme with high efficiency and accuracy, i.e., F1-score as high as 0.9. Finally, we also show that the proposed method is a practical solution even without the prior knowledge of the printer model is unavailable. To benefit the academic society, the resource of our work is online available at http://shorturl.at/jxELP.