StegPDF-21: A Benchmark Dataset for PDF Steganography Detection

---

Overview

StegPDF-21 is a benchmark dataset developed for machine learning-based PDF steganography detection. The dataset consists of clean and steganographic PDF documents generated using multiple information-hiding techniques and is intended to support research in:

• PDF Steganalysis
• Digital Forensics
• Cybersecurity
• Document Security
• Explainable Artificial Intelligence (XAI)
• Machine Learning Benchmarking

Unlike image and audio steganography, publicly available benchmark datasets for PDF steganography are extremely limited. StegPDF-21 addresses this gap by providing engineered feature representations extracted from PDF document structures.

---

Dataset Statistics

Property	Value
Dataset Name	StegPDF-21
Total Samples	19,372
Clean PDFs	9,621
Stego PDFs	9,751
Engineered Features	25
Classes	2
Labels	Clean (0), Stego (1)

---

Repository Structure

StegPDF-21/
│
├── extraction.py
├── stego_generation.py
├── README.md
├── LICENSE

---

Steganographic Techniques

Stego PDF documents were generated using multiple embedding strategies.

• Metadata Hiding
• Invisible (White) Text Insertion
• Text Spacing Manipulation
• Zero-Width Unicode Characters
• PDF Comment Injection
• Unused PDF Objects
• Stream Padding
• Embedded Image Steganography

---

Engineered Features

The dataset contains 25 engineered features extracted from each PDF document.

Structural Features

• File Size
• Page Count
• Object Count
• Average Objects per Page
• Orphan Object Count
• Orphan Object Depth
• Unused Object Ratio
• Cross Reference Gap Score
• Structural Complexity Score
• Page Object Distribution Entropy

---

Metadata Features

• Metadata Length
• Metadata Key Count
• Custom Metadata Key Count
• Metadata Value Entropy

---

Text Features

• Zero Width Unicode Density
• Invisible Text Ratio
• Character Spacing Deviation
• Whitespace Run Variance
• Text-to-Nontext Ratio

---

Comment Features

• Comment Object Count
• Comment Length Ratio

---

Stream Features

• Padding Byte Ratio

---

Image Features

• Image Count
• Image Entropy Delta
• Image Size Anomaly

---

Feature Extraction

The repository provides an automated feature extraction pipeline.

PDF Document
      │
      ▼
PDF Parsing
      │
      ▼
Metadata Analysis
      │
      ▼
Structure Analysis
      │
      ▼
Text Analysis
      │
      ▼
Image Analysis
      │
      ▼
Feature Engineering
      │
      ▼
CSV Dataset

---

Machine Learning Benchmark

The dataset was evaluated using representative machine learning classifiers.

Classifier	Accuracy	Precision	Recall	F1-score	ROC-AUC
Logistic Regression	0.6487	0.6459	0.6688	0.6572	0.7111
Gaussian Naïve Bayes	0.6024	0.6838	0.3910	0.4975	0.6901
Random Forest	0.7978	0.8137	0.7761	0.7945	0.8799
XGBoost	0.8109	0.8290	0.7867	0.8073	0.8900
LightGBM	0.8083	0.8302	0.7785	0.8035	0.8922
Support Vector Machine	(Coming Soon)

---

Evaluation Protocol

The benchmark experiments followed a standardized evaluation protocol.

• Stratified 70:30 Train-Test Split
• Stratified 5-Fold Cross Validation
• Optuna Hyperparameter Optimization
• Median Pruner
• Independent Test Evaluation

Performance metrics include

• Accuracy
• Precision
• Recall
• F1-score
• ROC-AUC

---

Requirements

Python ≥ 3.10

Required packages

numpy
pandas
PyPDF2
pikepdf
scikit-learn
xgboost
lightgbm
optuna
joblib
matplotlib
seaborn

Install dependencies

pip install -r requirements.txt

---

Usage

Generate Stego PDFs

python stego_generation.py

---

Extract Features

python extraction.py

The extracted features are automatically stored as

output/features_25_FINAL.csv

---

Applications

StegPDF-21 can be used for

• PDF Steganography Detection
• Machine Learning Research
• Digital Forensics
• Cybersecurity Research
• Explainable AI
• Benchmark Dataset Evaluation
• Feature Selection
• Classification Research

---

Citation

If you use this dataset in your research, please cite:

Amaan Hamid et al.

StegPDF-21: A Benchmark Dataset for PDF Steganography Detection.

Data in Brief.

(Under Review)

---

License

This project is licensed under the Apache License 2.0.

See the LICENSE file for details.

---

Author

Mohd. Amaan Hamid

M.Sc. Cyber Security Researcher

Research Interests

• PDF Steganography
• Digital Forensics
• Machine Learning
• Explainable AI
• Cybersecurity

GitHub

https://github.com/amn2905

---

Acknowledgements

This dataset was developed to facilitate reproducible research in PDF steganography detection and digital document forensics by providing a standardized benchmark for evaluating machine learning models.