Watermarking for Security Issue of Handwritten Documents with Fully Convolutional Networks

To prevent falsification of handwriting document images, the methods of forensic document examination are widely used to determine the origin and authenticity of a given document. In this paper, we propose an effective approach for security issue of handwriting documents in spatial domain by making use of watermarking technique. To begin with, the handwritten document is pre-processed by replacing gray level values holding high intensity with the mean value of document content. The document is then transformed into standard form to minimize geometric distortion. Next, fully convolutional networks (FCN) is leveraged to detect document's watermarking regions used for hiding secret information wherein an approach of FCN for document layout segmentation is adjusted to solve the problem of watermarking region detection. Lastly, the data hiding process is conducted by dividing gray level values of each connected object situated within watermarking regions into two sets for carrying one watermark bit. The experiments are performed on various handwritten documents, and our approach achieves high performance regarding such properties as imperceptibility and robustness against distortions caused by JPEG compression, geometric transformation and print-and-scan process.