Distortion Model-Based Spectral Augmentation for Generalized Recaptured Document Detection

Document recapturing is a presentation attack that covers the forensic traces in the digital domain. Document presentation attack detection (DPAD) is an important step in the document authentication pipeline. Existing DPAD methods suffer from low generalization performance under the cross-domain scenario with different types of documents. Data augmentation is a de facto technique to reduce the risk of overfitting the training data and improve the generalizability of a trained model. In this work, we improve the generalization performance of DPAD approaches by addressing two important limitations of the existing frequency domain augmentation (FDA) methods. First, contrary to the existing FDA methods that treat different spectral bands equally, we establish a band-of-interest localization (BOIL) method that locates the spectral band-of-interest (BOI) related to the recapturing operation by domain knowledge from the theoretical distortion models. Second, we propose a frequency-domain halftoning augmentation (FHAG) strategy that enhances the halftoning features in the BOI with considerations of different halftoning distortions. To evaluate the generalization performance of our FHAG with BOIL method on different types of document images, we have constructed a diverse recaptured document image dataset with 162 types of documents (RDID162), consisting of 5346 samples. The proposed method has been evaluated on the generic deep learning models and a state-of-the-art DPAD approach under both cross-device and cross-domain protocols for the DPAD task. Compared to the existing FDA methods, our method has improved the models with ResNet50 backbone by reducing more than 25% or 5 percentage points in EERs. The source code and data in this work is available at https://github.com/chenlewis/FHAG-with-BOIL.