italoaa/Cropped_BOSSBase_1.01_WOW_S-UNIWARD

Dataset Card: BOSS-Based Cropped Steganography Dataset

Dataset Overview

• —Name: BOSS-Based Cropped Steganography Dataset (working title)
• —Description: This dataset is a prepared and cropped version of the BOSSbase v1.01 dataset, commonly used in steganography and steganalysis research. Each original 512×512 grayscale image is split into four non-overlapping 256×256 patches to support deep learning experiments with smaller, manageable image sizes. The dataset supports binary classification tasks such as cover vs. stego detection.
• —Purpose: Designed to enable reproducibility of research in image steganalysis.
• —Supported Tasks:
• —Binary classification (cover vs. stego)
• —Steganalysis model evaluation
• —Author: Italo Amaya
• —License: MIT License
• —Intended Use: Academic research only

Dataset Structure

• —Total Images:
• —20,000 cover images
• —20,000 WOW stego images
• —20,000 S-UNIWARD stego images (Each stego set is embedded from the same 20,000 cover images, using different algorithms and randomized keys.)
• —Image Format: .png
• —Image Size: 256×256 pixels
• —Color: Grayscale (1 channel)
• —Directory Layout:

  cropdataset/
  ├── cover/
  └── stego/
      ├── S-UNIWARD/
      │   └── 0.4bpp/
      │       └── stego/
      └── WOW/
          └── 0.4bpp/
              └── stego/

• —Labels: Not explicitly stored — labels should be inferred during data loading (cover = 0, stego = 1)

Data Generation Process

• —Original Dataset: BOSSbase v1.01
• —Cropping Script (Python):

  from PIL import Image
  import os
  from tqdm import tqdm

  def crop_images(input_folder, output_folder, patch_size=(256, 256)):
      os.makedirs(output_folder, exist_ok=True)
      for filename in tqdm(os.listdir(input_folder), desc="Cropping"):
          try:
              input_path = os.path.join(input_folder, filename)
              if os.path.isfile(input_path):
                  img = Image.open(input_path)
                  if img.size == (512, 512):
                      w, h = patch_size
                      patches = {
                          "top_left": img.crop((0, 0, w, h)),
                          "top_right": img.crop((512-w, 0, 512, h)),
                          "bottom_left": img.crop((0, 512-h, w, 512)),
                          "bottom_right": img.crop((512-w, 512-h, 512, 512)),
                      }
                      for key, patch in patches.items():
                          patch.save(os.path.join(output_folder, f"{os.path.splitext(filename)[0]}_{key}.png"))
          except Exception as e:
              print(f"Error processing {filename}: {e}")

• —Embedding Tools:

[!NOTE] The embedding was done using random keys and as such this dataset is a random key dataset

• —WOW
• —S-UNIWARD Both are official C++ implementations from the University of Binghamton.
• —Embedding Parameters:
• —Randomized keys per image
• —0.4 bits per pixel (bpp)

Preprocessing & Transformations

• —Cropping: Non-overlapping 256×256 patches from 512×512 originals
• —Augmentation: None
• —Normalization: None — images retain original pixel value ranges

Citation and Attribution

• —GitHub Repository: [Link to be added once public]
• —Associated Paper: [Placeholder for paper title or DOI] Please cite this work when using the dataset.