---
license: apache-2.0
task_categories:
- image-classification
- unconditional-image-generation
tags:
- steganography
- aigc
- text-to-image
- deepfake-detection
- image-forensics
size_categories:
- 10K<n<100K
---

# AIGC Image Steganography Dataset

## 📖 Dataset Description

This dataset is specifically designed for research in Artificial Intelligence Generated Content (AIGC) image steganography, steganalysis, and image forensics. 

To construct a highly diverse and standardized dataset, we selected **10 prominent domestic and international text-to-image (T2I) large models** and batch-generated the images via their official APIs. 

During the generation process, we carefully defined **10 typical image styles**. For each style, every model was tasked with generating 500 images featuring diverse content. This systematic approach resulted in a comprehensive dataset of **50,000 images** in total (10 models × 10 styles × 500 images).

To ensure experimental uniformity and rigorously evaluate the performance of steganography algorithms, the resolution of all generated images was strictly fixed at **1024 × 1024**. This prevents resolution variations from introducing unwanted interference into steganographic experiments.

## 🤖 Source Models

The 50,000 images in this dataset were generated by the following 10 advanced T2I models:
1. **Baidu (百度)**
2. **LiblibAI (liblib)**
3. **Seedream 3.0 (火山大模型)**
4. **Seedream 4.0 (火山大模型)**
5. **Star3 Batch**
6. **Tencent Hunyuan (混元)**
7. **Kuaishou Kling (可灵)**
8. **Alibaba Qwen (千问)**
9. **iFLYTEK Spark (讯飞)**
10. **Zhipu AI (智谱)**

## 📁 Dataset Structure

The dataset is organized into 10 main directories based on the source T2I models. Each directory contains the generated images for that specific model.

```text
AIGC_Image_Steganography_Dataset/
├── img_baidu/              # 5,000 images
├── img_liblib/             # 5,000 images
├── img_seedream3.0/        # 5,000 images
├── img_seedream4.0/        # 5,000 images
├── img_star3_batch/        # 5,000 images
├── img_混元/               # 5,000 images
├── img_可灵/               # 5,000 images
├── img_千问/               # 5,000 images
├── img_讯飞/               # 5,000 images
└── img_智谱/               # 5,000 images
```


## 🚀 How to Use (Python)

You can easily download and load this dataset using the Hugging Face `datasets` library or the `huggingface_hub` tool.

### Method 1: Using the `datasets` library (Recommended for ML pipelines)
First, install the library in your terminal:
```bash
pip install datasets
```

Then, load the dataset in your Python script:
```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Asketla/AIGC_Image_Steganography_Dataset")

# Print the dataset information
print(dataset)
```

### Method 2: Downloading raw files via `huggingface_hub`
If you prefer to download the raw image folders directly to your local machine:
First, install the library in your terminal:
```bash
pip install huggingface_hub
```

Then, run the following Python script:
```python
from huggingface_hub import snapshot_download

# Download the entire dataset repository to a local directory
local_dir = snapshot_download(
    repo_id="Asketla/AIGC_Image_Steganography_Dataset",
    repo_type="dataset",
    local_dir="./aigc_steganography_data", # Specify your desired local path
    max_workers=4 # Adjust based on your network speed
)

print(f"Dataset successfully downloaded to: {local_dir}")
```