StegPDF-21: A Feature-Engineered Dataset for PDF Steganography Detection
Description
StegPDF-21 is a structured dataset designed for research in PDF steganography detection and document-level steganalysis. The dataset contains feature-based representations of clean and steganographic PDF documents for machine learning applications. Clean samples are labeled as 0, while steganographic samples containing intentionally embedded hidden data are labeled as 1. To improve dataset traceability and reproducibility, each document is associated with a unique file_id, enabling consistent identification and mapping of samples throughout preprocessing, feature extraction, and evaluation stages.
Files
Steps to reproduce
• Collect PDF documents from the PDF Association Stressful Corpus. • Remove corrupted, encrypted, malformed, and duplicate files. • Select a subset of clean PDFs as the base dataset. • Generate steganographic documents using multiple embedding techniques with different payload variants. • Extract structural and statistical features using Python libraries (pypdf/PyPDF2, NumPy, Pandas). • Perform correlation analysis and remove redundant features. • Assign binary labels (0 = clean, 1 = stego). • Store the final dataset in CSV format.