README.md

metadata

license: apache-2.0
pipeline_tag: image-classification
tags:
  - computer-vision
  - image-classification
  - pytorch
library_name: pytorch

Official repository for the paper "Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models"(https://arxiv.org/pdf/2602.01738)

If you have any questions, please feel free to open a discussion in the Community tab. For direct inquiries, you can also reach out to us via email at 2450042008@mails.szu.edu.cn.

VFM Baselines Release

This directory contains the 7 vision foundation model baselines used in the paper:

• MetaCLIP-Linear
• MetaCLIP2-Linear
• SigLIP-Linear
• SigLIP2-Linear
• PE-CLIP-Linear
• DINOv2-Linear
• DINOv3-Linear

Contents

• models.py: unified model-loading code for all 7 baselines
• test_vfm_baselines.py: unified evaluation script
• weights/: released checkpoints
• core/vision_encoder/: vendored PE vision encoder code required by PE-CLIP-Linear

Model Names

The unified loader and test script accept these names:

• metacliplin
• metaclip2lin
• sigliplin
• siglip2lin
• pelin
• dinov2lin
• dinov3lin

The paper names such as MetaCLIP-Linear and DINOv3-Linear are also accepted.

Usage

Evaluate a single model:

python test_vfm_baselines.py \
  --model sigliplin \
  --real-dir /path/to/0_real \
  --fake-dir /path/to/1_fake \
  --max-samples 100

Evaluate all 7 models:

python test_vfm_baselines.py \
  --model all \
  --real-dir /path/to/0_real \
  --fake-dir /path/to/1_fake \
  --max-samples 100

Optional arguments:

• --checkpoint: override the default checkpoint for single-model evaluation
• --batch-size: batch size for evaluation
• --num-workers: dataloader workers
• --device: explicit device such as cuda:0 or cpu
• --save-json: save results to a JSON file

Dependencies

The release code expects these Python packages:

• torch
• torchvision
• transformers
• scikit-learn
• Pillow
• timm
• einops
• ftfy
• regex
• huggingface_hub

Notes

• The clip-family and DINO-family baselines instantiate the backbone from Hugging Face model configs and then load the released checkpoint.
• PE-CLIP-Linear uses the vendored core/vision_encoder code in this directory.
• The checkpoints in weights/ are arranged locally for packaging convenience. For public release, they can be uploaded as the same filenames.