[Paper Review] Fast and Effective Robustness Certification for Recurrent Neural Networks.

Wonryong Ryou, Jiayu Chen+4 more|arXiv (Cornell University)|May 27, 2020

Adversarial Robustness in Machine Learning17 references4 citations

TL;DR

This paper introduces R2, a fast and scalable verifier for recurrent neural networks (RNNs) that uses tight linear convex relaxations via sampling and optimization, along with multi-bound optimization per neuron. R2 enables the first robustness certification for non-trivial RNN applications, such as speech classification, outperforming prior methods by an order of magnitude in speed while successfully verifying robustness across multiple RNN architectures in vision and speech domains.

ABSTRACT

We present a precise and scalable verifier for recurrent neural networks, called R2. The verifier is based on two key ideas: (i) a method to compute tight linear convex relaxations of a recurrent update function via sampling and optimization, and (ii) a technique to optimize convex combinations of multiple bounds for each neuron instead of a single bound as previously done. Using R2, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. This required us to also develop custom convex relaxations for the general operations that make up speech preprocessing. Our evaluation across a number of recurrent architectures in computer vision and speech domains shows that these networks are out of reach for existing methods as these are an order of magnitude slower than R2, while R2 successfully verified robustness in many cases.

Motivation & Objective

• To address the lack of effective robustness certification methods for recurrent neural networks, especially in complex, real-world applications.
• To develop a scalable and precise verification framework capable of handling the non-linear dynamics inherent in RNNs.
• To enable robustness certification for non-trivial RNN use cases, such as speech classification, which previous methods could not handle due to computational infeasibility.
• To design custom convex relaxations tailored to common speech preprocessing operations to support end-to-end verification.

Proposed method

• R2 computes tight linear convex relaxations of the recurrent update function using sampling-based optimization to improve bound quality.
• It employs a novel technique to optimize convex combinations of multiple bounds per neuron, improving over single-bound approaches used in prior work.
• The method integrates custom convex relaxations for general operations in speech preprocessing pipelines to support end-to-end verification.
• R2 leverages these relaxations within a scalable verification framework that maintains precision while reducing computational overhead.
• The framework is designed to handle diverse RNN architectures in both computer vision and speech domains.
• It uses iterative optimization to refine bounds, balancing tightness and computational efficiency.

Experimental results

Research questions

• RQ1 Can a scalable and precise verification method be developed for recurrent neural networks that outperforms existing approaches in speed and coverage?
• RQ2 How can tight convex relaxations be effectively computed for the non-linear update functions in RNNs using sampling and optimization?
• RQ3 Can multi-bound optimization per neuron significantly improve verification precision compared to single-bound methods?
• RQ4 Is robustness certification feasible for real-world RNN applications such as speech classification, given the complexity of preprocessing pipelines?
• RQ5 To what extent can R2 verify robustness in diverse RNN architectures across vision and speech domains where prior methods fail?

Key findings

• R2 successfully certifies robustness for recurrent neural networks in speech classification, a non-trivial and previously infeasible use case.
• The method achieves robustness certification for multiple RNN architectures in both computer vision and speech domains.
• R2 is an order of magnitude faster than existing verification methods, making it practical for real-world deployment.
• The use of convex combinations of multiple bounds per neuron leads to tighter and more precise verification compared to single-bound approaches.
• Custom convex relaxations for speech preprocessing operations enable end-to-end verification in speech classification tasks.
• R2 demonstrates scalability and precision where prior methods fail due to excessive computational cost.