A Robust Data Hiding Scheme Using Generated Content for Securing Genuine Documents Data hiding is an effective technique, compared to pervasive blackandwhite code patterns such as barcode and quick response code, which can be used to secure document images against forgery or unauthorized intervention. In this work, we propose a robust digital watermarking scheme for securing genuine documents by leveraging generative adversarial networks (GAN). To begin with, the input document is adjusted to its right form by geometric correction. Next, the generated document is obtained from the input document by using the mentioned networks, and it is regarded as a reference for data hiding and detection. We then introduce an algorithm that hides a secret information into the document and produces a watermarked document whose content is minimally distorted in terms of normal observation. Furthermore, we also present a method that detects the hidden data from the watermarked document by measuring the distance of pixel values between the generated and watermarked document. For improving the security feature, we encode the secret information prior to hiding it by using pseudo random numbers. Lastly, we demonstrate that our approach gives high precision of data detection, and competitive performance compared to stateoftheart approaches.

Hiding Security Feature Into Text Content for Securing Documents Using Generated Font Motivated by increasing possibility of the tampering of genuine documents during a transmission over digital channels, we focus on developing a watermarking framework for determining whether a given document is genuine or falsified. The proposed framework is performed by hiding a security feature or secret information within the document. In order to hide the security feature, we replace the appropriate characters of legal document by the equivalent characters coming from generated fonts, called hereafter the variations of characters. These variations are produced by training generative adversarial networks (GAN) with the features of characteru0027s skeleton and normal shape. Regarding the process of detecting hidden information, we make use of fully convolutional networks (FCN) to produce salient regions from the watermarked document. The salient regions mark positions of document where the characters are substituted by their variations, and these positions are used as a reference for extracting the hidden information. Lastly, we demonstrate that our approach gives high precision of data detection, and competitive performance compared to stateoftheart approaches.