Verification of Image-based Neural Network Controllers Using Generative Models

Understand

Neural networks are often used to process information from image-based sensors to produce control actions.

• While they are effective for this task, the complex nature of neural networks makes their output difficult to verify and predict, limiting their use in safety-critical systems.
• For this reason, recent work has focused on combining techniques in formal methods and reachability analysis to obtain guarantees on the closed-loop performance of neural network controllers.
• However, these techniques do not scale to the high-dimensional and complicated input space of image-based neural network controllers.