arsath-sm/face_classification_model2

Inception-style Face Classification Model 🎭

This model uses an Inception-style architecture to distinguish between real human faces and AI-generated faces.

Model Description 📝

Model Architecture

• —Inception-style network with multi-scale feature processing
• —Input shape: (224, 224, 3)
• —Multiple inception modules with parallel pathways
• —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: 52.94%
• —Loss: 0.6913

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
• —Inception modules: [64, 128, 256, 512] filters
• —Dense layer: 256 units

Evaluation Results 📊

Performance Metrics

• —Validation Accuracy: 52.94%
• —Validation Loss: 0.6913

Advantages Over ResNet Model

• —Better generalization
• —More stable learning process
• —Lower validation loss
• —Slightly higher accuracy

Usage 💻

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

# Load the model
model = load_model('face_classification_model2')

# 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. 1.Current performance limitations:
2. 2.Accuracy only slightly above random chance
3. 3.May require ensemble methods for better results
4. 4.Limited testing on diverse datasets

1. 1.Recommended improvements:
2. 2.Extended training with larger datasets
3. 3.Implementation of data augmentation
4. 4.Hyperparameter optimization
5. 5.Transfer learning from pre-trained models

Citation 📚

@software{face_classification_model2,
  author = {Your Name},
  title = {Face Classification Model using Inception Architecture},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/arsath-sm/face_classification_model2}
}

Contributors 👥

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

University of Jaffna

License 📄

This model is licensed under the MIT License.