Mirror DDA checkpoint assets

AIORSCAM_MIRROR.md

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+# AiorScam DDA Mirror
+
+This directory is the complete local staging area for the Hugging Face model repository `aiorscam/dda`.
+
+## Contents
+
+- `DDA_ckpt.pth`: official DDA checkpoint from `Junwei-Xi/Dual-Data-Alignment`.
+- `UPSTREAM_README.md`: upstream Hugging Face model card copied at mirror time.
+- `licenses/`: upstream Apache-2.0 licenses for DDA and DINOv2.
+- `SHA256SUMS`: checksums for mirrored files.
+- `mirror_manifest.json`: machine-readable provenance.
+
+No standalone DINOv2 weights are mirrored. The inference image uses pinned DINOv2 source for architecture construction and loads this DDA checkpoint strictly.

DDA_ckpt.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b27a31d39374803ddeff02bfabb2be76e190b04300490cddfafb24f683f37e3e
+size 1255621296

README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- ai-generated-image-detection
+- aiorscam
+base_model:
+- facebook/dinov2-large
+---
+# AiorScam DDA Mirror
+
+This repository mirrors the runtime checkpoint for the AiorScam DDA custom inference container.
+
+- Upstream code: https://github.com/roy-ch/Dual-Data-Alignment
+- Upstream checkpoint source: https://huggingface.co/Junwei-Xi/Dual-Data-Alignment
+- Checkpoint file: `DDA_ckpt.pth`
+- AiorScam inference image: `docker.io/aiorscam/dda-inference:0.1.0`
+
+The model is promptless. The endpoint loads `DDA_ckpt.pth` strictly into a DINOv2 ViT-L/14 + LoRA architecture and interprets `sigmoid(logit)` as the synthetic/fake probability.
+
+See `AIORSCAM_MIRROR.md` and `mirror_manifest.json` for mirror provenance.

SHA256SUMS

@@ -0,0 +1,8 @@
+99d33534f69b1eca8cd588eded0242f1b922bac182e381fdd6b0c6cc6dbcddd3  AIORSCAM_MIRROR.md
+b27a31d39374803ddeff02bfabb2be76e190b04300490cddfafb24f683f37e3e  DDA_ckpt.pth
+336fd8da1ced2016627b06798b805804f1d4ed6ffb2ec31c2747f41143c2f66f  README.md
+eb2b025ee2103e1dcb84880144b1952a80a14224016000c2d21fb83f39697f54  SHA256SUMS
+d0e708c37ea051f7ba63e6f5dc93146078748191c979f02511a4c0cd7ba217b0  UPSTREAM_README.md
+600cc67cc4cb2f5ea317dcfc687ad1c74dc4bec8782bbe9db0afd83513b935b7  licenses/DINOv2-LICENSE
+c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4  licenses/Dual-Data-Alignment-LICENSE
+3d9d57b37b43b0f3cfe02de9e4c4ba1ef83361eb2023b902e2cbc4a42b62cc00  mirror_manifest.json

UPSTREAM_README.md

@@ -0,0 +1,51 @@
+---
+license: apache-2.0
+datasets:
+- Junwei-Xi/DDA-Training-Set
+---
+# 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**.
+
+[![GitHub](https://img.shields.io/badge/GitHub-Project%20Page-black?logo=github)](https://github.com/roy-ch/Dual-Data-Alignment)
+[![arXiv](https://img.shields.io/badge/arXiv-2505.14359-b31b1b.svg)](https://arxiv.org/abs/2505.14359)
+
+## 📄 Model Details
+
+- **Model File**: `DDA_ckpt.pth`
+- **Paper**: [Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable](https://arxiv.org/abs/2505.14359)
+
+## 🚀 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:
+```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](https://openreview.net/forum?id=C39ShJwtD5)}
+}
+```

licenses/DINOv2-LICENSE

@@ -0,0 +1,203 @@
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licenses/Dual-Data-Alignment-LICENSE

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+                                 Apache License
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+
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mirror_manifest.json

@@ -0,0 +1,21 @@
+{
+  "dinov2_source_commit": "7b187bd4df8efce2cbcbbb67bd01532c19bf4c9c",
+  "dinov2_source_repo": "https://github.com/facebookresearch/dinov2",
+  "dinov2_standalone_weights_mirrored": false,
+  "files": [
+    "AIORSCAM_MIRROR.md",
+    "DDA_ckpt.pth",
+    "README.md",
+    "UPSTREAM_README.md",
+    "licenses/DINOv2-LICENSE",
+    "licenses/Dual-Data-Alignment-LICENSE"
+  ],
+  "license": "apache-2.0",
+  "mirror_repo": "aiorscam/dda",
+  "source_checkpoint_file": "DDA_ckpt.pth",
+  "source_checkpoint_repo": "Junwei-Xi/Dual-Data-Alignment",
+  "source_checkpoint_revision": "4390d9023899196b437480bb6a441915ef5d816c",
+  "staged_at": "2026-05-28T14:47:58.699365Z",
+  "upstream_code_commit": "8b9c06e75e63f4688bc25ac43a7e3412878cf67f",
+  "upstream_code_repo": "https://github.com/roy-ch/Dual-Data-Alignment"
+}