A distortion model guided adversarial surrogate for recaptured document detection

Abstract

Due to the ever-growing need for e-business, document presentation attack detection (DPAD) is an important forensic task. With deep learning, the performances of DPAD methods have been improved significantly. However, the cross-domain performance under different types of document images is not yet satisfactory. In this work, we propose to study the document recapturing distortion model (DM), which is employed in guiding the training of an end-to-end surrogate distortion model. This surrogate model is incorporated into the DPAD scheme in an adversarial training fashion, yielding the proposed Adversarial Surrogate-based DPAD (AS-DPAD) scheme. The surrogate model actively generates adversarial samples with recapturing distortions that confuse the DPAD model. Meanwhile, the DPAD backbone learns a latent space that considers the distances between the generated and real recaptured samples to achieve a better generalization performance. A challenging cross-domain protocol is conducted in our experiment. The results confirm that our DM improves the efficacy of the trained surrogate distortion model and also validate that the proposed adversarial training strategy leads to a significant performance gain in the evaluation of document images with different contents.