corvi-2022-mirror

Corvi 2022 — DMimageDetection (mirror)

Unmodified mirror of the two Corvi 2022 detector weights from grip-unina/DMimageDetection, hosted here for reliable consumption from a Hugging Face Space.

Grag2021_progan/model_epoch_best.pth     # ResNet-50 stride1, trained on ProGAN outputs
Grag2021_latent/model_epoch_best.pth     # ResNet-50 stride1, trained on latent-diffusion outputs

Each file is ~282 MB. Architecture: ResNet-50 with stride0=1, gap_size=1, num_classes=1. Output is a per-image logit; positive = fake. ImageNet normalization, no resize / crop required.

Reference

R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, L. Verdoliva. On the detection of synthetic images generated by diffusion models. ICASSP 2023. https://doi.org/10.1109/ICASSP49357.2023.10095167

License

Apache 2.0 — same as upstream. Weights are byte-identical to the ones distributed at the upstream Google Drive link (https://drive.google.com/file/d/1sAoAuOGCWS4dAMBhDkRHgBf4SgBgvkVf).

Sanity numbers

On 5 random uniform PIL noise images of varied sizes (a basic end-to-end-wiring check, not a benchmark):

Negative means "real / not synthetic", which is the expected response to structureless noise.

Cross-architecture observations (GPT-Image-2, 2026-04)

We measured these weights on GPT-Image-2 (autoregressive) outputs in a diagnostic study; full numbers and methodology are in the companion Space:

FPR @ logit=0 ≤ 0.4% on real (FFHQ-500 + COCO-500), so the detectors remain well-calibrated on natural-photo real reference.