Junwei-Xi/Dual-Data-Alignment

Dual Data Alignment (NeurIPS'25 Spotlight)

This repository contains the official checkpoint (DDA_ckpt.pth) for the paper "Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable", accepted by NeurIPS 2025 as a Spotlight.

📄 Model Details

• Model File: DDA_ckpt.pth
• Paper: Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable

🚀 Performance

DDA achieves state-of-the-art performance across 11 benchmarks, including 4 in-the-wild datasets.

Benchmark	NPR (CVPR'24)	UnivFD (CVPR'23)	FatFormer (CVPR'24)	SAFE (KDD'25)	C2P-CLIP (AAAI'25)	AIDE (ICLR'25)	DRCT (ICML'24)	AlignedForensics (ICLR'25)	DDA (ours)
GenImage (1G + 7D)	51.5 ± 6.3	64.1 ± 10.8	62.8 ± 10.4	50.3 ± 1.2	74.4 ± 8.4	61.2 ± 11.9	84.7 ± 2.7	79.0 ± 22.7	91.7 ± 7.8
DRCT-2M (16D)	37.3 ± 15.0	61.8 ± 8.9	52.2 ± 5.7	59.3 ± 19.2	59.2 ± 9.9	64.6 ± 11.8	90.5 ± 7.4	95.5 ± 6.1	98.1 ± 1.4
DDA-COCO (5D)	42.2 ± 5.4	52.4 ± 1.5	51.7 ± 1.5	49.9 ± 0.3	51.3 ± 0.6	50.0 ± 0.4	60.2 ± 4.3	86.5 ± 19.1	92.2 ± 10.6
EvalGEN (3D + 2AR)	2.9 ± 2.7	15.4 ± 14.2	45.6 ± 33.1	1.1 ± 0.6	38.9 ± 31.2	19.1 ± 11.1	77.8 ± 5.4	68.0 ± 20.7	97.2 ± 4.2
Synthbuster (9D)	50.0 ± 2.6	67.8 ± 14.4	56.1 ± 10.7	46.5 ± 20.8	68.5 ± 11.4	53.9 ± 18.6	84.8 ± 3.6	77.4 ± 25.0	90.1 ± 5.6
ForenSynths (11G)	47.9 ± 22.6	77.7 ± 16.1	90.0 ± 11.8	49.7 ± 2.7	92.0 ± 10.1	59.4 ± 24.6	73.9 ± 13.4	53.9 ± 7.1	81.4 ± 13.9
AIGCDetectionBenchmark (7G + 10D)	53.1 ± 12.2	72.5 ± 17.3	85.0 ± 14.9	50.3 ± 1.1	81.4 ± 15.6	63.6 ± 13.9	81.4 ± 12.2	66.6 ± 21.6	87.8 ± 12.6
Chameleon (Unknown)	59.9	50.7	51.2	59.2	51.1	63.1	56.6	71.0	82.4
Synthwildx (3D)	49.8 ± 10.0	52.3 ± 11.3	52.1 ± 8.2	49.1 ± 0.7	57.1 ± 4.2	48.8 ± 0.8	55.1 ± 1.8	78.8 ± 17.8	90.9 ± 3.1
WildRF (Unknown)	63.5 ± 13.6	55.3 ± 5.7	58.9 ± 8.0	57.2 ± 18.5	59.6 ± 7.7	58.4 ± 12.9	50.6 ± 3.5	80.1 ± 10.3	90.3 ± 3.5
Bfree-Online (Unknown)	49.5	49.0	50.0	50.5	50.0	53.1	55.7	68.5	95.1
Avg ACC	46.1 ± 16.1	56.3 ± 16.5	59.6 ± 14.6	47.6 ± 16.0	62.1 ± 15.6	54.1 ± 12.8	70.1 ± 14.6	75.0 ± 11.1	90.7 ± 5.3
Min ACC	2.9	15.4	45.6	1.1	38.9	19.1	50.6	53.9	81.4

Notably, DDA is the first detector to achieve over 80% cross-data accuracy on the Chameleon benchmark.

📚 Citation

If you find this model useful, please cite our paper:

@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)}
}