Recapture detection is critical in forensic tasks, especially for authentication of face and document images in electronic Know Your Customer (e-KYC) processes. While deep learning has advanced face anti-spoofing (FAS) and document presentation attack detection (DPAD), weak forensic cues still hinder reliability. We propose the Forensicability Assessment Network (FANet) to assess sample forensicability and reject low-forensicability samples before recapture detection. This enhances the overall performance and reliability of e-KYC systems. FANet operates independently, without relying on real training data or actual forensic features, ensuring strong generalization across various scenarios. It combines image quality and forensic task cues, defining three forensicability classes based on domain knowledge. FANet is trained with cross-entropy loss, updating centers using a momentum-based approach. Experimental results demonstrate significant improvements in reliability by filtering out low-forensicability samples. This work introduces the first comprehensive approach to assessing forensicability, ensuring more reliable recapture detection in face and document images. The source codes are available at https://github.com/chenlewis/FANet.

With the ongoing popularization of online services, the digital document images have been used in various applications. Meanwhile, there have emerged some deep learning-based text editing algorithms which alter the textual information of an image in an end-to-end fashion. In this work, we present a low-cost document forgery algorithm by the existing deep learning-based technologies to edit practical document images. To achieve this goal, the limitations of existing text editing algorithms towards complicated characters and complex background are addressed by a set of network design strategies. First, the unnecessary confusion in the supervision data is avoided by disentangling the textual and background information in the source images. Second, to capture the structure of some complicated components, the text skeleton is provided as auxiliary information and the continuity in texture is considered explicitly in the loss function. Third, the forgery traces induced by the text editing operation are mitigated by some post-processing operations which consider the distortions from the print-and-scan channel. Quantitative comparisons of the proposed method and the exiting approach have shown the advantages of our design by reducing the about 2/3 reconstruction error measured in MSE, improving reconstruction quality measured in PSNR and in SSIM by 4 dB and 0.21, respectively. Qualitative experiments have confirmed that the reconstruction results of the proposed method are visually better than the existing approach in both complicated characters and complex texture. More importantly, we have demonstrated the performance of the proposed document forgery algorithm under a practical scenario where an attacker is able to alter the textual information in an identity document using only one sample in the target domain. The forged-and-recaptured samples created by the proposed text editing attack and recapturing operation have successfully fooled some existing document authentication systems.