Xicor9/efficientnet-b0-ffpp-c23

🧠 EfficientNet-B0 Deepfake Detector (FaceForensics++ C23)

👤 Author

Himanshu Kashyap (Xicor9)
MSc Advanced Computer Science
University of Strathclyde, Glasgow

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📘 Overview

This repository contains a fine-tuned EfficientNet-B0 model for deepfake detection trained on the FaceForensics++ (FF++) C23 dataset. The model classifies face images or frames into:

• 0 → Real
• 1 → Fake

The training data includes multiple manipulation types such as DeepFake, FaceSwap, Face2Face, and NeuralTextures. The model is designed for academic and research use.

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📊 Performance

Frame-Level Metrics

Metric	Score
AUC	0.933
AP	0.898
Accuracy	0.852
F1-Score	0.843

Video-Level Metrics

(Mean probability across frames)

Metric	Score
AUC	0.94+
Accuracy	0.88+

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🏗 Model Architecture & Training Setup

• Base model: EfficientNet-B0 (ImageNet pretrained)
• Final classification layer: Linear(in_features, 2)
• Loss function: CrossEntropyLoss
• Optimizer: AdamW (lr = 3e-4, weight_decay = 1e-4)
• LR Scheduler: CosineAnnealingLR (T_max = 10)
• Mixed Precision (AMP): Enabled
• Epochs: 8
• Hardware: Tesla T4 (Google Colab)

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🔧 How to Use This Model

Load the model

import torch
import torch.nn as nn
from torchvision import models

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load state dict from Hugging Face
state_dict = torch.hub.load_state_dict_from_url(
    "https://huggingface.co/Xicor9/efficientnet-b0-ffpp-c23/resolve/main/efficientnet_b0_ffpp_c23.pth",
    map_location=device
)

# Rebuild architecture
model = models.efficientnet_b0(weights=None)
model.classifier[1] = nn.Linear(model.classifier[1].in_features, 2)

model.load_state_dict(state_dict)
model.to(device)
model.eval()

📷 Example Inference

from PIL import Image
from torchvision import transforms

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

img = Image.open("frame.jpg").convert("RGB")
x = transform(img).unsqueeze(0).to(device)

with torch.no_grad():
    logits = model(x)
    prob_fake = torch.softmax(logits, dim=1)[0][1].item()

print("Fake Probability:", prob_fake)

🗄 Training Dataset

This model was trained on the FaceForensics++ (FF++) C23 dataset.

Included Video Types

• Real videos
• Manipulated videos, including:
  • DeepFake
  • FaceSwap
  • Face2Face
  • NeuralTextures

All videos were converted into frames and resized to 224 × 224 before training.

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💡 Intended Use

This model is designed for:

• Deepfake research
• Academic coursework and university projects
• Benchmarking deepfake detection models
• Dataset experimentation and model comparison

❗ Not intended for real-world forensic, biometric, surveillance, or law-enforcement applications.

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📎 License

This model is released strictly for research and educational purposes only.
Users must ensure compliance with dataset and model licenses before use.

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❤️ Acknowledgements

• FaceForensics++ dataset authors
• PyTorch / TorchVision development teams
• EfficientNet authors
• Google Colab for compute resources