A Watermarking Technique Based on File Page Objects for PDF This paper presents a novel algorithm which is based on the Portable Document Format (PDF) document structure. The PDF document page objects are utilized for watermarking. The secret message is compressing by using Huffman coding, then suitable page objects of PDF document are used for hiding the secret message. Since the embedded watermark information is stored in PDF document page objects, so it will not affect the content, as well as the format of the PDF documents. The experimental result shows that the proposed algorithm is robust with detection accuracy can be up to 97%. The watermark is extracted with high probability, which illustrates that the proposed algorithm is robust and imperceptible. The experiments prove that the proposed method is suitable for PDF documents protection.

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.