Scalable Polyhedral Verification of Recurrent Neural Networks
This addresses the need for reliable verification of recurrent neural networks in safety-critical applications like speech and vision, representing an incremental advance in verification methods.
The paper tackles the problem of verifying recurrent neural networks by developing Prover, a scalable verifier that uses polyhedral abstractions and refinement algorithms, achieving the first certification of non-trivial use cases like speech classification and verifying challenging models beyond prior work.
We present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and nonlinear recurrent update functions by combining sampling, optimization, and Fermat's theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.