Deepfake Video Classifier
🎬 Detect manipulated videos with 95.73% accuracy
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 49,7 MB
- Versão
- vFfpp efficientnet best
- Atualizado
- 24 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
Deepfake Video Classifier
🎬 Detect manipulated videos with 95.73% accuracy
This model analyzes video frames to determine if content is REAL or a DEEPFAKE. It is Trained on Celebdf v2 dataset and it uses efficientnet B-0. (https://www.kaggle.com/datasets/reubensuju/celeb-df-v2) Developed by Sajjal Fatima, a Software Engineering student at Punjab University College of Information & Technology (PUCIT), Lahore, Pakistan.
🚀 Quick Start
from model import DeepFakeModel
from utils import video_to_tensor
# Load model
model = DeepFakeModel("ffpp_efficientnet_best.pth")
# Process video
video_tensor = video_to_tensor("your_video.mp4")
result = model.predict(video_tensor)
print(f"Prediction: {result['prediction']}") # REAL or FAKE
print(f"Confidence: {result['confidence']:.2%}")
Fontes
1 fonte
Fontes
1 fonteVerificado 24 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- license: mit tags: - deepfake-detection - video-classification - efficientnet - celeb df v2 pipeline_tag: video-classification widget: - src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/thumbnail.png example_title: "Sample Detection" --- # Deepfake Video Classifier 🎬 **Detect manipulated videos with 95.73% accuracy** This model analyzes video frames to determine if content is REAL or a DEEPFAKE. It is Trained on Celebdf v2 dataset and it uses efficientnet B-0. (https://www.kaggle.com/datasets/reubensuju/celeb-df-v2) Developed by Sajjal Fatima, a Software Engineering student at Punjab University College of Information & Technology (PUCIT), Lahore, Pakistan. ## 🚀 Quick Start ```python from model import DeepFakeModel from utils import video_to_tensor # Load model model = DeepFakeModel("ffpp_efficientnet_best.pth") # Process video video_tensor = video_to_tensor("your_video.mp4") result = model.predict(video_tensor) print(f"Prediction: {result['prediction']}") # REAL or FAKE print(f"Confidence: {result['confidence']:.2%}")
Source context: 0 downloads · 0 likes · Pipeline video-classification · Repo oo01/deepfake-video-classifier
