Rinovative/pd12m_dct_based_synthetic_stegano

PD12M DCT-Based Synthetic Steganography Dataset

This dataset is a synthetically generated steganographic image dataset based on the PD12M (Public Domain 12M) image collection.
It simulates detectable modifications produced by real-world JPEG steganography algorithms, using only public domain data.

Each original image is duplicated into three synthetic stego variants, inspired by real-world JPEG steganographic algorithms:

• synthetic_JMiPOD: simulated using conseal.nsF5, as JMiPOD is not available in conseal
• synthetic_JUNIWARD: generated via conseal.juniward
• synthetic_UERD: generated via conseal.uerd

All variants were created using the official embedding simulators of the conseal library.
The embedding strength was set to alpha = 0.4.

Each Stego image contains a simulated payload embedded using conseal.
The payload is not retained or accessible and serves only to produce realistic DCT modifications.

📂 Dataset Structure

/Cover/                 → Original PD12M images (resized + JPEG recompressed)
/synthetic_JMiPOD/      → Cost-based embedding (via nsF5)
/synthetic_JUNIWARD/    → Texture-aware HF embedding
/synthetic_UERD/        → Uniform random embedding

All images are JPEG-compressed and named using a uniform index (e.g., 00001.jpg).

📜 License and Sources

• Original PD12M Dataset: Public Domain / CC0
  source.plus/pd12m

• This synthetic dataset: CC0

• conseal simulation library: MPL-2.0 License
  uibk-uncover/conseal

📚 Citation

If you use this dataset, please reference:

• The original PD12M image dataset (Public Domain)
• This synthetic modification (Rinovative, 2025)
• The conseal simulation library

  Lorch, B., & Benes, M. (2024). conseal: Simulation framework for JPEG steganography. University of Innsbruck.
  GitHub repository: uibk-uncover/conseal

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📄 Project Reference

Note:
This synthetic dataset was created as a publicly available demonstration subset for the project:
"Deep Learning for Steganalysis – ALASKA2 Dataset"
(GitHub Repository – Rinovative/alaska2-steganalysis)
(BSc Systems Engineering, Computational Engineering Major, OST – Spring 2025, Rino Albertin).

The project primarily uses the ALASKA2 dataset.
In cases where ALASKA2 is not available, this synthetic dataset provides a compatible fallback for running the full pipeline.

Important:
This demo subset is fixed and remains consistent with the final project version.