Deep Discriminative Feature Learning for Document Image Manipulation Detection

Kais Rouis, Petra Gomez-Krämer, Mickaël Coustaty, Saddok Kebairi, Vincent Poulain D'Andecy

Abstract: Image authenticity analysis has become a very important task in the last years with one main objective that is tracing the counterfeit content induced by illegal manipulations and forgeries that can be easily practiced using available software tools. In this paper, we propose a reliable residual-based deep neural network that is able to detect document image manipulations and copy-paste forgeries. We consider the perceptual characteristics of documents including mainly textual regions with homogeneous backgrounds. To capture abstract features, we introduce a shallow architecture using residual blocks and take advantage of shortcut connections. A first layer is implemented to boost the model performance, which is initialized with high-pass filters to forward low-level error feature maps. Manipulation experiments are conducted on a publicly available document dataset. We compare our method with two interesting forensic approaches that incorporate deep neural models along with first lay