---
license: apache-2.0
library_name: pytorch
pipeline_tag: image-classification
tags:
- aigc-detection
- ai-generated-image-detection
- image-forensics
- pytorch
- dinov3
- clip
- icml-2026
---
# 🔍 ForensicConcept
### Transferable Forensic Concepts for AIGI Detection
Official detector checkpoints for the ICML 2026 paper
**ForensicConcept: Transferable Forensic Concepts for AIGI Detection**.
[[`Code`](https://github.com/EthanAdamm/FORENSICCONCEPT)]
[[`Paper`](https://arxiv.org/abs/2606.07034)]
[[`Models`](https://huggingface.co/ethan225/ForensicConcept/tree/main/weights)]
[[`BibTeX`](#citation)]
[](https://www.apache.org/licenses/LICENSE-2.0)
[](https://pytorch.org/)
[](https://icml.cc/)
---
## Model Overview
ForensicConcept converts diffuse detector evidence into explicit, auditable
forensic concepts and transfers these concepts across detector backbones. The
framework combines:
- **Adapter-guided discriminative tuning (ADT)** for efficient detector
adaptation with LoRA;
- **Unsupervised concept induction (UCI)** for discovering a compact forensic
concept codebook from decision-critical image patches;
- **Concept-aligned projection (CAP)** for concept-space prediction;
- **Concept-guided codebook injection (CGCI)** for transferring
diffusion-derived generation traces to target backbones.
## Performance
Image-level accuracy (%) reported in the paper. Models are trained on Stable
Diffusion 1.4 images.
| Evaluation benchmark | DINOv3 without concepts | ForensicConcept |
|---|---:|---:|
| GenImage, mean over 8 generators | 90.7 | **92.0** |
| GAN-family, mean over 7 generators | 87.3 | **90.1** |
| Chameleon | 83.7 | **84.4** |
| Mean over the three benchmarks | 87.2 | **88.8** |
The transferred codebook also raises CLIP ViT-L/14 mean accuracy on GenImage
from **83.7** to **88.2** (+4.5 points).
## Available Files
| File | Purpose | Size |
|---|---|---:|
| `weights/detectors/dinov3_vitl16_lora_stage1.pth` | DINOv3 ADT / LoRA initialization | 24.3 MiB |
| `weights/detectors/dinov3_vitl16_concept_stage2.pth` | DINOv3 ForensicConcept inference | 26.6 MiB |
| `weights/detectors/clip_vitl14_lora_stage1.pth` | CLIP LoRA initialization | 4.6 MiB |
| `weights/detectors/clip_vitl14_codebook_stage2.pth` | CLIP CGCI inference | 20.5 MiB |
| `weights/concepts/dinov3_concept_matrix.npy` | DINOv3 concepts (`200 x 1024`, float32) | 0.8 MiB |
The four detector checkpoints total **76.0 MiB**. Stage-1 checkpoints are
provided for reproducing stage-2 training. For inference, use the
corresponding stage-2 checkpoint. DINOv3 inference also requires the released
concept matrix.
## Quick Start
### 1. Clone the code
```bash
git clone https://github.com/EthanAdamm/FORENSICCONCEPT.git
cd FORENSICCONCEPT
python -m pip install -r requirements.txt
```
### 2. Download the released weights
Run this command from the root of the cloned code repository:
```bash
hf download ethan225/ForensicConcept \
--include "weights/**" \
--local-dir .
```
### 3. Add the external backbones
The pretrained DINOv3 ViT-L/16 and CLIP ViT-L/14 backbones are not included.
Download them from their official upstream projects and use this layout:
```text
weights/
|-- backbones/
| |-- ViT-L-14.pt
| `-- dinov3-vitl16/
| `-- model.safetensors
|-- concepts/
| `-- dinov3_concept_matrix.npy
`-- detectors/
|-- dinov3_vitl16_lora_stage1.pth
|-- dinov3_vitl16_concept_stage2.pth
|-- clip_vitl14_lora_stage1.pth
`-- clip_vitl14_codebook_stage2.pth
```
### 4. Evaluate
```bash
# DINOv3 ForensicConcept
python test_with_config.py \
--config configs/dinov3_concept.yaml \
--checkpoint_path weights/detectors/dinov3_vitl16_concept_stage2.pth
# CLIP with concept-guided codebook injection
python test_with_config.py \
--config configs/clip_codebook.yaml \
--checkpoint_path weights/detectors/clip_vitl14_codebook_stage2.pth
```
Dataset paths are placeholders in the public configurations. Update
`data.*` and `testing.groups` before evaluation. See the
[`GitHub README`](https://github.com/EthanAdamm/FORENSICCONCEPT#dataset-preparation)
for complete installation, dataset, training, and evaluation instructions.
## Checkpoint Notes
- DINOv3 stage 2 contains the classifier, concept mapping/head, and all LoRA
tensors. The external concept matrix initializes the model structure before
loading the checkpoint.
- CLIP stage 2 contains visual LoRA, the main classifier, and the complete
codebook head.
- External backbone weights remain subject to their respective upstream
licenses and terms.
SHA-256 checksums
```text
3bc8e833d75c9ccbb214c28238c791681294ae8b0c50e48cc3e64b9e5ac5ca1f weights/detectors/dinov3_vitl16_lora_stage1.pth
99162dbf6a56610be5a8bb9fa27e62311f723d0a4f34e384b084e76085491aad weights/detectors/dinov3_vitl16_concept_stage2.pth
b0c0217b547391a70eed680ef0d8f92555d172241c46bea767346c8d65115184 weights/detectors/clip_vitl14_lora_stage1.pth
a81ebec9281aca3db4b0d88254d25424f37d5d498ae2c405eb6a5be4748dc498 weights/detectors/clip_vitl14_codebook_stage2.pth
9e6ce224d223dab804648bb46a9cc405bfef4cfcb13dc9cc21ad7fd94935959c weights/concepts/dinov3_concept_matrix.npy
```
## Citation
```bibtex
@inproceedings{zhou2026forensicconcept,
title = {{ForensicConcept}: Transferable Forensic Concepts for {AIGI} Detection},
author = {Zhou, Menyanshu and Zhou, Ziyin and Sun, Ke and Luo, Yunpeng and Ji, Jiayi and Sun, Xiaoshuai and Ji, Rongrong},
booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
year = {2026}
}
```
## License
The ForensicConcept detector checkpoints are released under the
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). External
backbone weights are not included and remain subject to their respective
upstream licenses and terms.