---
title: DDA-Training-Set
canonical_url: "https://www.modelscope.cn/datasets/JunweiXi/DDA-Training-Set"
md_url: "https://www.modelscope.cn/datasets/JunweiXi/DDA-Training-Set.md"
repository: JunweiXi/DDA-Training-Set
last_updated: 2025-12-16
license: "Apache License 2.0"
storage_size: "105 GB"
downloads: 5779
stars: 0
---

# DDA-Training-Set

> DDA-Training-Set - JunweiXi 在 ModelScope 开源的数据集。Official Dataset for Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable Conference: 39th Conference on Neural Information Processing Systems (NeurIPS 2025) https://arxiv.org/abs/2505.14359

JunweiXi/DDA-Training-Set 是 ModelScope 魔搭社区上的数据集，存储大小 105 GB，采用 Apache License 2.0 许可。

- **Repository**: JunweiXi/DDA-Training-Set
- **License**: Apache License 2.0
- **Storage size**: 105 GB
- **Downloads**: 5779
- **Stars**: 0
- **Last updated**: 2025-12-16

Source: https://www.modelscope.cn/datasets/JunweiXi/DDA-Training-Set

---

# DDA Training Set

### Official Dataset for **Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable**
**Conference:** 39th Conference on Neural Information Processing Systems (NeurIPS 2025) https://arxiv.org/abs/2505.14359

---

#### Dataset Description
This dataset serves as the core training data for the paper **"Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable"**. 

It is designed to address **Format Bias**, **Content Bias**, and **Size Bias** prevalent in traditional AIGI detection datasets. Constructed based on the **MSCOCO** training set, this dataset utilizes the **Dual Data Alignment (DDA)** technique to generate synthetic images, ensuring that "real" and "fake" images are highly aligned in both the pixel and frequency domains.

#### Composition
* **Real Images:** Sourced from the MSCOCO training set.
* **Synthetic Images:** Corresponding DDA-aligned synthetic images for each real image.

#### Dataset Details & Formatting
The training dataset is stored in the directory `DDA-COCO_TrainSet/`.

* **File Format:** **PNG** (Lossless).
* **Preprocessing Logic:**
    * **Spatial Alignment:** We crop each real image so that its height and width are **multiples of 8**. This step is crucial to ensure that VAE reconstructions are perfectly aligned with the original images in spatial resolution.
    * **Avoiding Format Bias:** All real and fake images are strictly saved in **PNG format**. If the cropped real images were re-saved as JPEG, they would undergo **double-JPEG compression**. This would introduce additional compression artifacts and undesirable format bias, potentially causing the detector to learn the compression history rather than the generation artifacts.

#### Citation
```bibtex
@inproceedings{chen2025dual,
  title={Dual Data Alignment Makes {AI}-Generated Image Detector Easier Generalizable},
  author={Ruoxin Chen and Junwei Xi and Zhiyuan Yan and Ke-Yue Zhang and Shuang Wu and Jingyi Xie and Xu Chen and Lei Xu and Isabel Guan and Taiping Yao and Shouhong Ding},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
  year={2025},
  url={[https://openreview.net/forum?id=C39ShJwtD5](https://openreview.net/forum?id=C39ShJwtD5)}
}