Abstract: Described are methods and systems for detecting fraud in documents. First images of a first set of genuine documents and second images of a second set of genuine documents are obtained. A printed feature, spacings between printed features in the first images, and positions of printed features in the second images are selected. Selected features, spacings and positions are annotated to obtain original landmark locations for each printed feature, spacing and position. Annotated features, spacings and positions are transformed to obtain transformed features, transformed spacings and transformed positions. The transformed features, spacings and positions are combined with a noise model to generate modified features, modified spacings and modified positions. Each modified feature, modified spacing and modified position comprises annotations indicating modified landmark locations. Input data for a machine learning model is generated using original landmark locations and modified landmark locations.

Abstract: Described are methods and systems for detecting fraud in documents. First images of a first set of genuine documents and second images of a second set of genuine documents are obtained. A printed feature, spacings between printed features in the first images, and positions of printed features in the second images are selected. Selected features, spacings and positions are annotated to obtain original landmark locations for each printed feature, spacing and position. Annotated features, spacings and positions are transformed to obtain transformed features, transformed spacings and transformed positions. The transformed features, spacings and positions are combined with a noise model to generate modified features, modified spacings and modified positions. Each modified feature, modified spacing and modified position comprises annotations indicating modified landmark locations. Input data for a machine learning model is generated using original landmark locations and modified landmark locations.