2 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a vi…
Towards Robust Speech-to-Text Adversarial Attack
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
This paper introduces a novel adversarial algorithm for attacking the state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo. Our approach is based on develo…
Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
This paper introduces a defense approach against end-to-end adversarial attacks developed for cutting-edge speech-to-text systems. The proposed defense algorithm has four major ste…
Cyclic Defense GAN Against Speech Adversarial Attacks
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
This paper proposes a new defense approach for counteracting state-of-the-art white and black-box adversarial attack algorithms. Our approach fits into the implicit reactive defens…
Conditioning Trick for Training Stable GANs
Mohammad Esmaeilpour, Raymel Alfonso Sallo, Olivier St-Georges +2
In this paper we propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training. W…
Class-Conditional Defense GAN Against End-to-End Speech Attacks
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
In this paper we propose a novel defense approach against end-to-end adversarial attacks developed to fool advanced speech-to-text systems such as DeepSpeech and Lingvo. Unlike con…