Adjust-free Adversarial Example Generation In Speech Recognition Using Evolutionary Multi-objective Optimization Under Black-box Condition

Abstract

This paper proposes a black-box adversarial attack method to automatic speech recognition systems. Some studies have attempted to attack neural networks for speech recognition; however, these methods did not consider the robustness of generated adversarial examples against timing lag with a target speech. The proposed method in this paper adopts Evolutionary Multi-objective Optimization (EMO)that allows it generating robust adversarial examples under black-box scenario. Experimental results showed that the proposed method successfully generated adjust-free adversarial examples, which are sufficiently robust against timing lag so that an attacker does not need to take the timing of playing it against the target speech.

Stats

• citations7
• S2 citations—
• github stars0
• HF likes0
• heat score6.77
• arxiv keyishida2020adjust

Related papers

• Targeted Adversarial Examples For Black Box Audio Systems (2018)15.75
• Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness (2018)0.00
• Training Augmentation With Adversarial Examples For Robust Speech Recognition (2018)11.39
• Zero-query Adversarial Attack On Black-box Automatic Speech Recognition Systems (2024)8.60
• Multi-discriminator Sobolev Defense-gan Against Adversarial Attacks For End-to-end Speech Systems (2021)8.82
• Inaudible Adversarial Perturbations For Targeted Attack In Speaker Recognition (2020)12.33
• Adversarial Attacks Against Automatic Speech Recognition Systems Via Psychoacoustic Hiding (2018)16.45
• Adversarial Attack And Defense Strategies For Deep Speaker Recognition Systems (2020)13.39