CLIP_aievals: AI–Generated Image Detector

This model is a CLIP-based classifier fine-tuned to detect AI-generated images across a wide range of generative models. It is trained using a mixture of real datasets (FFHQ, COCO, ImageNet, AFHQ, etc.) and synthetic datasets from diffusion, GANs, and hybrid architectures.

Overview

CLIP_aievals is designed for robust AI-vs-Real detection by leveraging a CLIP Vision Transformer backbone and a lightweight classification head. It is optimized for generalization across unseen generative sources and large-scale evaluation pipelines.

This repository contains the model weights (clip_vith14_argus.pt) and supporting configuration files used for inference.

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Model Architecture

Backbone

• CLIP ViT-H/14 vision encoder
• Pretrained on LAION-2B
• Frozen or partially unfrozen depending on training configuration

Classifier Head

• Two-layer MLP:

  • Input: CLIP image embedding (1024-d)
  • Hidden Layer: 512 with GELU activation
  • Output Layer: 1-unit sigmoid classifier producing probability of AI-generated content

Regularization and Calibration

• Dropout: 0.1
• Weight decay: 1e-4
• Temperature calibration performed post-hoc using validation logits
• Optional threshold tuning using Eval metrics or Unknown-source analysis

Training Objective

• Binary cross-entropy
• Oversampling and class-balancing for multi-source synthetic datasets

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Datasets

The training pipeline uses a mixture of curated datasets:

Real Data

• FFHQ (70k)
• COCO (160k)
• ImageNet (90k+)
• AFHQ v1/v2 (cats, dogs, wildlife)
• DIV2K
• OpenImages

Fake Data

• Stable Diffusion (v1.x, v2.x)
• Latent Diffusion Models
• StyleGAN3
• CIPS
• BigGAN
• GANformer
• CycleGAN (horse2zebra, monet2photo)
• DDPM and DDGAN
• Face Synthetics
• Glide
• Generative Inpainting (partial and full)

Labels are binary: 0 = real, 1 = fake.

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Performance Summary

Evaluated on 850k+ mixed-source images:

• ROC-AUC: 0.764
• PR-AUC (AI class): 0.612
• Global FPR (real images): 0.0073
• Accuracy: 0.693
• Precision (AI): 0.853
• Recall (AI): 0.086

Performance is dataset-dependent: high confidence on many synthetic sources, lower recall on advanced diffusion models exhibiting strong photorealism.

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Intended Use

Primary

• Detect whether an image is AI-generated
• Large-scale offline evaluation of generative models
• Data filtering for dataset curation
• Quality and authenticity control in multimedia pipelines

Secondary

• Research on generative model detection
• Cross-model robustness evaluation

Not Intended For

• Legal or forensic verification
• High-stakes decision systems
• Per-pixel or localized artifact detection

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Limitations

• Lower recall on highly realistic diffusion models.

• Model can produce false positives on:

  • Overprocessed images
  • Heavy JPEG compression
  • Artistic filters

• Not calibrated for forensic authenticity analysis.

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How to Use

In Python

from src.model import AIImageDetector
from PIL import Image
import torch

model = AIImageDetector(
    clip_model_name="ViT-H-14",
    device="cuda",
    dropout=0.1
)

model.load_state_dict(torch.load("clip_vith14_argus.pt", map_location="cpu"))
model.eval()

img = Image.open("your_image.jpg")
prob = model.predict(img)  # returns probability of AI generation
print(prob)