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sarvansh/
NotUrFace-AI

Name: NotUrFace-AI
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NotUrFace-AI: Deepfake Detection Model

Model Details

Model Description

NotUrFace-AI is a deepfake detection model designed to classify video content as real or fake. It processes first 30-50 video frames using TensorFlow and applies advanced machine learning techniques to identify synthetic or manipulated media. This is a passion project aimed at combating deepfake proliferation. The model is particularly useful for:

Social media content moderation
Digital forensics
Research in deepfake detection and AI ethics

Developer: Sarvansh Pachori
Model Type: Deepfake detection (video-based classification)
Finetuned from: XceptionNet (pretrained)

Model Sources

Repository: sarvansh30/NotUrFace-AI
Demo: Hugging Face Space

Usage

Direct Use

Classifying videos as real or fake for research, moderation, or forensic purposes.

Downstream Use

The model can be fine-tuned with additional deepfake datasets for improved detection on specific video types.

Out-of-Scope Use

The model is not intended for legal decision-making or high-stakes scenarios where absolute certainty is required.

Bias, Risks, and Limitations

Accuracy may vary depending on dataset bias and the quality of input videos.
False positives or false negatives can occur, requiring human verification for critical applications.
It may struggle with detecting highly sophisticated, unseen deepfake techniques.

Recommendations

Users should validate outputs in real-world applications before making critical decisions.
Future improvements may include training on a more diverse dataset to reduce bias.

Getting Started

Use the following code snippet to get started:

python

from transformers import AutoModelForImageClassification, AutoTokenizer

model = AutoModelForImageClassification.from_pretrained("sarvansh/NotUrFace-AI")
tokenizer = AutoTokenizer.from_pretrained("sarvansh/NotUrFace-AI")

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was tested on unseen samples from the FaceForensics++ and CelebDFv2 datasets.

Metrics

Accuracy: Measures correct classifications.
F1 Score: Balances precision and recall.

Results

Metric	Value
Training Accuracy	98.44%
Validation Accuracy	97.05%
Test Accuracy	95.93%

Disclaimer: These results were obtained using the FaceForensics++ and CelebDFv2 datasets. Performance in real-world scenarios may vary.

Tips for Best Performance

The model works best with videos that have proper lighting.
It only analyzes the first 1-1.5 seconds of a video, so ensure the clip is appropriately selected for evaluation.

Model Architecture and Objective

Feature Extraction: XceptionNet (pretrained on ImageNet) to extract spatial features.
Temporal Analysis: LSTM layers to analyze frame dependencies.
Classification: Fully connected layers for final binary classification.

Citation

If using this model in research, please cite:

BibTeX:

@article{noturface-ai,
  author = {Sarvansh Pachori},
  title = {NotUrFace-AI: Deepfake Detection Model},
  year = {2024},
  journal = {Hugging Face Model Hub},
  url = {https://huggingface.co/sarvansh/NotUrFace-AI}
}

Contact Information

For any issues, improvements, or inquiries, contact:

Author: Sarvansh Pachori
Email: sarvansh.pachori45@gmail.com
My Github profile: sarvansh30