---
license: mit
tags:
  - deepfake-detection
  - image-classification
  - vision-transformer
  - dct
  - pytorch
datasets:
  - birdy654/cifake-real-and-ai-generated-synthetic-images
---

# Deepfake Detection — Hybrid ViT + DCT (CIFAKE)

Trained checkpoints for a deepfake image detection project using a hybrid
Vision Transformer + DCT frequency-domain architecture.

## Models

| Checkpoint | Architecture | Test Acc | Test AUC-ROC |
|---|---|---|---|
| `baseline_resnet50_best.pt` | ResNet-50 (fine-tuned) | — | — |
| `baseline_efficientnet_b4_best.pt` | EfficientNet-B4 (fine-tuned) | — | — |
| `dct_only_main_best.pt` | DCT-only CNN | — | — |
| `hybrid_main_best.pt` | Hybrid ViT-B/16 + DCT | — | — |

## Dataset

[CIFAKE: Real and AI-Generated Synthetic Images](https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images)
— 60K real (CIFAR-10) + 60K Stable Diffusion generated images.

## Usage

```python
import torch
from models.baseline_cnn import ResNet50Classifier

model = ResNet50Classifier(pretrained=False)
model.load_state_dict(torch.load("baseline_resnet50_best.pt", map_location="cpu"))
model.eval()
```

## Architecture

- **ViT branch**: `vit_base_patch16_224` (timm), CLS token → 768-dim
- **DCT branch**: Block-wise 2D DCT on 8×8 tiles → small CNN → 256-dim
- **Fusion**: concat(1024) → LayerNorm → Linear(512) → GELU → Dropout(0.3) → Linear(1)