Abstract
Verification of scanned document authenticity is important for digital archiving, information security, and forensic analysis. This paper proposes a reconstruction error-based method for detecting structural anomalies in scanned documents. A convolutional autoencoder is trained on authentic document patches and used to generate reconstruction error maps. These maps are then classified by a CNN to distinguish the original scans from the reproduced samples. Experiments on a controlled dataset show that reconstruction error maps provide discriminative information for detecting artefacts introduced by document reproduction. The results should be interpreted as a proof of concept for controlled reproduction detection rather than as a complete solution for all real-world forgery scenarios.
Paper Type
Poster
DOI
10.62036/ISD.2026.90
Reconstruction Error-Based Detection of Structural Anomalies in Scanned Documents Using Autoencoders and CNNs
Verification of scanned document authenticity is important for digital archiving, information security, and forensic analysis. This paper proposes a reconstruction error-based method for detecting structural anomalies in scanned documents. A convolutional autoencoder is trained on authentic document patches and used to generate reconstruction error maps. These maps are then classified by a CNN to distinguish the original scans from the reproduced samples. Experiments on a controlled dataset show that reconstruction error maps provide discriminative information for detecting artefacts introduced by document reproduction. The results should be interpreted as a proof of concept for controlled reproduction detection rather than as a complete solution for all real-world forgery scenarios.
Recommended Citation
Janik, M., Nowak, J., Korytkowski, M., Scherer, R., Stachowiak, S. & Witkowski, M.(2026). Reconstruction Error-Based Detection of Structural Anomalies in Scanned Documents Using Autoencoders and CNNs. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.90