Reversible data hiding in PDF files by overlapping characters
Year: 2026
Type: article
Abstract: Most reported PDF-based data hiding methods make subtle changes to existing PDF elements to conceal data, where the changes are predominantly made to the existing ‘TJ’ operator in a typical PDF document. However, this choice limits the volume of data that can be hidden, and most methods inevitably introduce visual distortion to the PDF document. Therefore, in this work, we propose a novel method called OPAC to hide data in PDF by means of o verla p ping char ac ters , which is the first of its kind for data hiding purposes. Specifically, overlapping employs a series of technical steps to strategically stack characters. First, all 26 characters in the English alphabet are arranged into a 4 × 7 table based on their frequency of occurrences, where each character can be referred to by specifying the column and row in which it appears. Subsequently, the encrypted message is hidden, one character at a time, by superimposing a special symbol called anchor onto a specific character to indicate the column number, and specific combination of the ‘Tf’ and ‘Tz’ operators is added to the PDF stream to indicate the row number. Meanwhile, the newly added ‘Tf’ and ‘Tz’ operators are further manipulated to reduce the size and width of anchor to allow for complete character overlapping. We evaluate the performance of the proposed method OPAC using 220 PDF documents generated from various famous texts. On average, OPAC can hide 3877 characters at the expense of 0.224 MB increase in PDF file size. Furthermore, experiment results confirmed that OPAC achieves distortion-free data hiding and reversibility. Moreover, OPAC is empirically verified to be resistant to several common PDF processing and alterations.
Institution: Monash University Malaysia
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,130
Award: G5809275523
OpenAlex ID: W7124866085
