---
title: DDA-COCO
canonical_url: "https://www.modelscope.cn/datasets/JunweiXi/DDA-COCO"
md_url: "https://www.modelscope.cn/datasets/JunweiXi/DDA-COCO.md"
repository: JunweiXi/DDA-COCO
last_updated: 2026-02-02
license: "Apache License 2.0"
storage_size: "4.0 GB"
downloads: 485
stars: 1
---

# DDA-COCO

> DDA-COCO - 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-COCO 是 ModelScope 魔搭社区上的数据集，存储大小 4.0 GB，采用 Apache License 2.0 许可。

- **Repository**: JunweiXi/DDA-COCO
- **License**: Apache License 2.0
- **Storage size**: 4.0 GB
- **Downloads**: 485
- **Stars**: 1
- **Last updated**: 2026-02-02

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

---

# DDA-COCO Benchmark

### 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
**DDA-COCO** is a benchmark specifically designed to evaluate whether AIGI detectors rely on **"non-causal features"** (such as compression artifacts or content semantics).

Many existing detectors experience significant performance drops when tested on strictly aligned data, as they tend to learn dataset biases rather than intrinsic generation artifacts. DDA-COCO includes real images from the MSCOCO validation set and their corresponding synthetic images, processed with various VAE reconstructions and frequency alignments, to test detector robustness.

#### Content
The dataset contains 5 subsets corresponding to different VAE model reconstructions:
* **Source:** MSCOCO Validation Set (Real).
* **Variations:** Synthetic images reconstructed by different VAE versions (e.g., SD1.5, SD2.1, SDXL) with frequency alignment.
* **Key Feature:** High consistency between real and synthetic images in semantics, size, and frequency distribution, forcing detectors to focus on subtle generative traces.

#### Citation
```code
@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}
}
```