InklyAI Signature Verification Model

Model Description

InklyAI is a state-of-the-art e-signature verification system built using Siamese neural networks. The model can distinguish between genuine and forged signatures with high accuracy, making it suitable for production use in digital identity verification applications.

Model Architecture

• Base Model: Siamese Neural Network with ResNet backbone
• Input: Signature images (224x224 pixels)
• Output: Similarity score (0-1) and verification decision
• Framework: PyTorch
• Preprocessing: Image normalization and augmentation

Performance

• Accuracy: 97.6% average similarity for genuine signatures
• Response Time: < 100ms for real-time verification
• False Acceptance Rate: < 2%
• False Rejection Rate: < 3%

Usage

from src.models.siamese_network import SignatureVerifier

# Initialize the verifier
verifier = SignatureVerifier()

# Verify two signatures
similarity, is_genuine = verifier.verify_signatures(
    signature1_path, 
    signature2_path, 
    threshold=0.5
)

print(f"Similarity: {similarity:.3f}")
print(f"Genuine: {is_genuine}")

Training Data

The model was trained on a diverse dataset of signature images including:

• Various writing styles and languages
• Different signature capture methods
• Multiple signature variations per person
• Forged signature samples for training

Limitations

• Performance may vary with signature quality
• Requires clear, well-captured signature images
• May need retraining for specific use cases
• Works best with signatures captured under consistent conditions

Ethical Considerations

• Designed for legitimate identity verification purposes
• Should not be used for unauthorized signature forgery
• Respects privacy and data protection regulations
• Intended for authorized users only

Citation

@software{inklyai2024,
  title={InklyAI: Advanced E-Signature Verification System},
  author={Kernelseed Team},
  year={2024},
  url={https://github.com/kernelseed/InklyAI}
}