An SMT-Based Approach for Verifying Binarized Neural Networks

Understand

Deep learning has emerged as an effective approach for creating modern software systems, with neural networks often surpassing hand-crafted systems.

• Unfortunately, neural networks are known to suffer from various safety and security issues.
• Formal verification is a promising avenue for tackling this difficulty, by formally certifying that networks are correct.
• We propose an SMT-based technique for verifying Binarized Neural Networks - a popular kind of neural network, where some weights have been binarized in order to render the neural network more memory and energy efficient, and quicker to evaluate.