Local Binary Patterns for Document Forgery Detection

Document forgery is an increasing problem for both the public administration and private companies. It represents substantial losses in time and economical resources. Classical solutions to this problem such as watermarks or other integrated security patterns can not be applied in general for any unknown incoming document due to the large variability on types of documents. In that scenario it is important to resort to forensic techniques to seek and analyze inconsistencies on the intrinsic features of the document image. In this paper we present a classification-based approach for forgery detection. We use uniform Local Binary Patterns (LBP) to capture discriminant texture features that are common on forged regions. Besides, we combine multiple descriptors from neighboring regions to model contextual information. Results using Support Vector Machines (SVM) for patch classification show that we are able to detect several types of forgeries in a wide range of types of documents.