Harshasnade/Deepfake_Detection_System_V1

DeepGuard - Deepfake Detection System

Model Details

Model Description

DeepGuard is a robust Deepfake Detection System designed to identify AI-generated images with high precision. It employs an ensemble architecture combining EfficientNetV2-S and Swin Transformer V2-T with a custom Convolutional Neural Network (CNN) head. This hybrid approach leverages both local feature extraction (CNN) and global context understanding (Transformers) to spot manipulation artifacts often invisible to the human eye.

• Developed by: Harshvardhan Asnade
• Model type: Ensemble (EfficientNetV2 + SwinV2 + Custom CNN)
• Language(s): Python, PyTorch
• License: MIT
• Finetuned from model: Torchvision pre-trained weights (ImageNet)

Model Sources

• Repository: https://github.com/Harshvardhan-Asnade/Deepfake-Model
• Demo: https://deepfakescan.vercel.app/ (Live Web App)

Uses

Direct Use

The model is designed to classify single images as either REAL or FAKE. It outputs a probability score (0.0 - 1.0) and a confidence metric. It is suitable for:

• Content moderation
• Social media verification
• Digital forensics (preliminary analysis)

Out-of-Scope Use

• Video Analysis: While it can analyze individual frames, it does not currently leverage temporal coherence in videos (frame-by-frame analysis only).
• Audio Deepfakes: This model is strictly for visual content.
• Legal Proof: The model provides a probabilistic assessment and should not be the sole basis for legal judgments.

How to Get Started with the Model

import torch
import torch.nn as nn
from torchvision import models
from safetensors.torch import load_file
import cv2

# Define Model Architecture
class DeepfakeDetector(nn.Module):
    def __init__(self, pretrained=False):
        super(DeepfakeDetector, self).__init__()
        self.efficientnet = models.efficientnet_v2_s(weights='DEFAULT' if pretrained else None)
        self.swin = models.swin_v2_t(weights='DEFAULT' if pretrained else None)
        
        self.efficientnet.classifier = nn.Identity()
        self.swin.head = nn.Identity()
        
        self.classifier = nn.Sequential(
            nn.Linear(1280 + 768, 512),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Dropout(0.4),
            nn.Linear(512, 1)
        )

    def forward(self, x):
        f1 = self.efficientnet(x)
        f2 = self.swin(x)
        combined = torch.cat((f1, f2), dim=1)
        return self.classifier(combined)

# Load Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DeepfakeDetector(pretrained=False).to(device)
state_dict = load_file("best_model.safetensors")
model.load_state_dict(state_dict)
model.eval()

Training Details

Training Data

The model was trained on a diverse dataset comprising:

• Real Images: FFHQ, CelebA-HQ
• Deepfake Images: Generated using StyleGAN2, Diffusion Models, and FaceSwap techniques.
• Data Augmentation: extensive augmentation (compression, noise, blur) was applied to robustify the model against social media re-compression artifacts.

Evaluation

Results

The model achieves high accuracy on standard benchmarks:

• Test Accuracy: ~92-95% (on unseen test split)
• Generalization: Shows strong resilience to JPEG compression compared to standard CNNs.

Citation

@misc{deepguard2024,
  author = {Asnade, Harshvardhan},
  title = {DeepGuard: Ensemble Deepfake Detection System},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Harshasnade/Deepfake_Detection_System_V1}}
}