arsath-sm/face_classification_model1

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language: en tags: - image-classification - computer-vision - deep-learning - face-detection - resnet datasets: - custom license: mit

ResNet-based Face Classification Model 🎭

This model is trained to distinguish between real human faces and AI-generated faces using a ResNet-based architecture.

Model Description 📝

Model Architecture

• Deep CNN with residual connections based on ResNet architecture
• Input shape: (224, 224, 3)
• Multiple residual blocks with increasing filter sizes [64, 128, 256, 512]
• Global average pooling
• Dense layers with dropout for classification
• Binary output with sigmoid activation

Task

Binary classification to determine if a face image is real (human) or AI-generated.

Framework and Training

• Framework: TensorFlow
• Training Device: GPU
• Training Dataset: Custom dataset of real and AI-generated faces
• Validation Metrics:
  • Accuracy: 52.45%
  • Loss: 0.7246

Intended Use 🎯

Primary Intended Uses

• Research in deepfake detection
• Educational purposes in deep learning
• Face authentication systems

Out-of-Scope Uses

• Production-level face verification
• Legal or forensic applications
• Stand-alone security systems

Training Procedure 🔄

Training Details

optimizer = Adam(learning_rate=0.0001)
loss = 'binary_crossentropy'
metrics = ['accuracy']

Training Hyperparameters

• Learning rate: 0.0001
• Batch size: 32
• Dropout rate: 0.5
• Architecture:
  • Initial conv: 64 filters, 7x7
  • Residual blocks: [64, 128, 256, 512] filters
  • Dense layer: 256 units

Evaluation Results 📊

Performance Metrics

• Validation Accuracy: 52.45%
• Validation Loss: 0.7246

Limitations

• Performance is only slightly better than random chance
• May struggle with high-quality AI-generated images
• Limited testing on diverse face datasets

Usage 💻

from tensorflow.keras.models import load_model
import cv2
import numpy as np

# Load the model
model = load_model('face_classification_model1')

# Preprocess image
def preprocess_image(image_path):
    img = cv2.imread(image_path)
    img = cv2.resize(img, (224, 224))
    img = img / 255.0
    return np.expand_dims(img, axis=0)

# Make prediction
image = preprocess_image('face_image.jpg')
prediction = model.predict(image)
is_real = prediction[0][0] > 0.5

Ethical Considerations 🤝

This model is designed for research and educational purposes only. Users should:

• Obtain proper consent when processing personal face images
• Be aware of potential biases in face detection systems
• Consider privacy implications when using face analysis tools
• Not use this model for surveillance or harmful applications

Technical Limitations ⚠️

1. Current performance limitations:

   • Accuracy only slightly above random chance
   • May require ensemble methods for better results
   • Limited testing on diverse datasets

2. Recommended improvements:

   • Extended training with larger datasets
   • Implementation of data augmentation
   • Hyperparameter optimization
   • Transfer learning from pre-trained models

Citation 📚

@software{face_classification_model1,
  author = {Your Name},
  title = {Face Classification Model using ResNet Architecture},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/arsath-sm/face_classification_model1}
}

Contributors 👥

• Arsath S.M
• Faahith K.R.M
• Arafath M.S.M

University of Jaffna

License 📄

This model is licensed under the MIT License.