🔍 ForensicConcept
Transferable Forensic Concepts for AIGI Detection
Official detector checkpoints for the ICML 2026 paper ForensicConcept: Transferable Forensic Concepts for AIGI Detection.
[Code]
[Paper]
[Models]
[BibTeX]
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
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:
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:
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
# 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
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.
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
@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. External backbone weights are not included and remain subject to their respective upstream licenses and terms.