metadata

language: en
license: mit
tags:
  - deepfake
  - video-classification
  - pytorch
  - computer-vision
datasets:
  - custom
pipeline_tag: video-classification
widget:
  - example_title: Deepfake Detection
    example_input: Upload a video to detect if it's real or fake

DeepFake Detection Model

This model detects deepfake videos using a combination of ResNext50 and LSTM architecture. It analyzes video frames to determine if a video is authentic or manipulated.

Model Details

Model Type: Video Classification
Task: Deepfake Detection
Framework: PyTorch
Training Data: Deepfake video datasets
Accuracy: 87% on test datasets
Output: Binary classification (real/fake) with confidence score

Usage

from transformers import pipeline

# Load the model
detector = pipeline("video-classification", model="tayyabimam/Deepfake")

# Analyze a video
result = detector("path/to/video.mp4")
print(result)

API Usage

You can also use this model through the Hugging Face Inference API:

import requests

API_URL = "https://api-inference.huggingface.co/models/tayyabimam/Deepfake"
headers = {"Authorization": "Bearer YOUR_API_TOKEN"}

def query(video_path):
    with open(video_path, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

result = query("path/to/video.mp4")
print(result)

Model Architecture

The model uses a ResNext50 backbone to extract features from video frames, followed by an LSTM to capture temporal relationships between frames. This architecture is particularly effective for detecting manipulation artifacts that appear across multiple frames in deepfake videos.

Limitations

The model works best with videos that include human faces
Performance may vary with different video qualities and resolutions
The model is designed for 20-frame sequences for optimal performance