Certifying Neural Network Audio Classifiers

Wonryong Ryou, Mislav Balunovic, Gagandeep Singh, Martin Vechev

TL;DR: We present the first approach to certify robustness of neural networks against noise-based perturbations in the audio domain.

Abstract: We present the first end-to-end verifier of audio classifiers. Compared to existing methods, our approach enables analysis of both, the entire audio processing stage as well as recurrent neural network architectures (e.g., LSTM). The audio processing is verified using novel convex relaxations tailored to feature extraction operations used in audio (e.g., Fast Fourier Transform) while recurrent architectures are certified via a novel binary relaxation for the recurrent unit update. We show the verifier scales to large networks while computing significantly tighter bounds than existing methods for common audio classification benchmarks: on the challenging Google Speech Commands dataset we certify 95% more inputs than the interval approximation (only prior scalable method), for a perturbation of -90dB.

Code: https://drive.google.com/file/d/13dFJb3hwFaMortWr3D_3Z5H4j6HhurAh/view?usp=sharing

Keywords: Adversarial Examples, Audio Classifier, Speech Recognition, Certified Robustness, Deep Learning