Deepfake Detection Cifake

--- 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)

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