MuskAI/LTF-v3.3

LTF — AI-generated image detection

Paper · Project page · Code

Try the online demo — upload an original image and click Run Analysis. The demo uses the Fusion branch at threshold 0.5 by default.

The demo also shows exact tile boundaries, L1 per-head tile attention, L2 view weights, top-four attention crops, within-tile MIL patch attention and read-only parameters. Attention shows relative model focus and is not a pixel-level forgery probability.

This repository contains the epoch 20 checkpoint used in the historical GenImage evaluation 20260326_012209_ViT-L-14_genimage_v3_v3_3.

LTF uses a frozen CLIP ViT-L/14 backbone, two-level multiple-instance learning (MIL), cross-shuffle during training, and pool-logit gated fusion. The repository includes the custom PyTorch runtime and the frozen backbone. It does not require conversion to a Transformers architecture.

Historical results

Fixed threshold 0.5, 8 GenImage subsets, 100,000 images. Accuracy is the unweighted mean across subsets, as recorded in the original rounded logs:

Branch	Accuracy
L1	95.26%
L2	97.39%
Fusion	98.39%

Per-subset metrics are in results/genimage8_logged_metrics.csv. These are historical logged results; uploading this repository did not rerun the complete benchmark. SD1.4 official validation images were used during training validation, so that subset overlaps model validation. These results do not represent eight entirely held-out generators.

Download and run

Use Python 3.11. The commands below use this model repository.

python -m pip install -U huggingface_hub
hf download MuskAI/LTF-v3.3 --local-dir ./LTF-v3.3
cd LTF-v3.3
python -m pip install -r requirements.txt
python inference.py --input /path/to/image.jpg --device cpu

For GPU inference, use --device cuda:0. For larger images, lower the CLIP forward chunk size without removing tiles:

python inference.py --input /path/to/image.jpg --device cuda:0 --chunk-size 8

Python API

Run from the downloaded repository directory:

from ltf_final import LTFDetector

detector = LTFDetector(device="auto", chunk_size=8)
result = detector.predict("image.jpg", branch="fusion", threshold=0.5)
print(result["prediction"], result["score_fake"])
print(result["probabilities"])

The image is decoded as RGB. Non-overlapping full 224×224 local blocks, with right/bottom boundary completion, plus a global view use CLIP preprocessing. Input files are not recompressed. Test tiling depends on image size. Labels are 0=real, 1=fake. The API returns L1/L2/fusion scores, the selected label and the view count. Loading the model does not download external weights after this repository has been downloaded. It uses backbones/ViT-L-14.pt.

Run the web demo locally

The tested Gradio UI is included in demo/. From the downloaded model directory:

python -m pip install -r demo/requirements.txt
LTF_MODEL_DIR="$PWD" python demo/app.py

Open http://localhost:7860. For a remote server, forward port 7860 over SSH. Upload an original file, click Run Analysis, and inspect fusion, L1 and L2 scores. The preview is for display only; inference uses the original file's bytes.

The demo also supports deployment as a separate Gradio Space. Set LTF_MODEL_REPO to this model ID and LTF_MODEL_REVISION to a commit SHA. Creating the online compute Space requires an eligible Hugging Face account. Publishing the model repository alone does not start a hosted inference service.

Checkpoint identity and provenance

weights/epoch_20.pth is the original core state_dict (not best.pth):

SHA256 32e9fa2e38e04d6d0221c844d650f0d45f7235bbf898b5ad665543d8be580031

Frozen CLIP backbone:

SHA256 b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836

release_manifest.json records checksums of every runtime file. The original training and evaluation directories did not save training-day source snapshots. The released core was recovered from git commit 65c18ee5868ced87a27d048261e237632ad5d704, with network naming adjusted to LTF. This verifies checkpoint identity and runtime compatibility, not bit-for-bit reproduction of the original training process. This Hub adaptation adds tensor-only checkpoint loading, preserves the model root in symlink-based Hub caches, and removes a machine-specific dataset path.

Source comments and docstrings are in English. This documentation update preserves the executable AST and checkpoint parameter files.

Limitations

Scores are not calibrated authenticity probabilities. Predictions can change with image content, generator, editing and compression. This model is a research detector; an individual score does not prove image provenance.

The public demo uses configurable image-size limits for its available compute; oversized uploads are rejected, not silently resized. CPU inference can be slow for images containing many local tiles.

License and citation

The official source repository is released under the MIT license. Example images retain the terms of their original datasets and rightsholders. Bundled CLIP components retain the upstream OpenAI CLIP license.

Please cite the LTF paper:

@inproceedings{chen2026ltf,
  title={Learning to Falsify: Hierarchical Multiple Instance Learning for High Resolution AI-Generated Image Detection},
  author={Chen, Haoran and Gao, Daiqi and Li, Xinyuan and Wang, Yongwei and Zhu, Lei and Cai, Rui and Dong, Jianfeng},
  booktitle={Proceedings of the 34th ACM International Conference on Multimedia},
  year={2026},
  doi={10.1145/3767308.3836365},
  eprint={2611.04529},
  archivePrefix={arXiv}
}

Tiling policy

Full 224×224 blocks are taken without overlap. If pixels remain at the right or bottom, a final block is aligned to that boundary; only boundary blocks may overlap. For width 500, horizontal starts are 0, 224, 276. The global view is separate. This corrects earlier uniformly overlapping tiling; weights are unchanged, but predictions can change. Historical benchmark results have not been recomputed with this preprocessing.