Moiré Spectral Augmentation and Masked Frequency Modeling for Document Presentation Attack Detection

Document Presentation Attack is an anti-forensic operation that conceals the forgery traces of image manipulation in the digital domain. Existing document presentation attack detection (DPAD) methods show unsatisfactory performance under samples with different contents and qualities. In this work, we focus on the DPAD task on screen-recapturing channel and exploit the prior knowledge of distortion (i.e., moire pattern) in the spectral domain to address these limitations. We propose a frequency-domain moir ´ e´ augmentation (FMAG) strategy that enhances the spectral components contributed to the moire distortion, improving the generalization ´ performance under different document contents. We devise the mask moire frequency modeling (M ´ 2FM) scheme to reconstruct the moire-related spectral components in low-quality samples under the guidance of the spectral distortion model and a pre-trained DPAD ´ classifier. To evaluate the generalization performance, we collect the diverse Screen Recaptured Document Image Dataset with 162 different document contents (SRDID162) consisting of 162 genuine document images, as well as 2592 low and high-quality recaptured document images, respectively. Our experimental protocol involves training with high-quality ID images and testing with SRDID162 dataset of diverse contents and image qualities. Compared to a SOTA data augmentation approach for recaptured natural images, our FMAG & M2FM approach achieves a significant improvement of 49.15% or 22.50 percentage points in average EER on the generic deep learning backbones. The data and code of this work will be available at Github