10 citations · 14 across the 8 of their papers we have counts for
9 papers
Clustering Unsupervised Representations as Defense against Poisoning Attacks on Speech Commands Classification System
Thomas Thebaud, Sonal Joshi, Henry Li +4
Poisoning attacks entail attackers intentionally tampering with training data. In this paper, we consider a dirty-label poisoning attack scenario on a speech commands classificatio…
Multi-Target Backdoor Attacks Against Speaker Recognition
Alexandrine Fortier, Sonal Joshi, Thomas Thebaud +3
In this work, we propose a multi-target backdoor attack against speaker identification using position-independent clicking sounds as triggers. Unlike previous single-target approac…
Clean Label Attacks against SLU Systems
Henry Li Xinyuan, Sonal Joshi, Thomas Thebaud +3
Poisoning backdoor attacks involve an adversary manipulating the training data to induce certain behaviors in the victim model by inserting a trigger in the signal at inference tim…
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification
Sonal Joshi, Thomas Thebaud, Jesús Villalba +1
Adversarial examples have proven to threaten speaker identification systems, and several countermeasures against them have been proposed. In this paper, we propose a method to dete…
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser
Sonal Joshi, Saurabh Kataria, Yiwen Shao +4
Adversarial attacks are a threat to automatic speech recognition (ASR) systems, and it becomes imperative to propose defenses to protect them. In this paper, we perform experiments…
AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification
Sonal Joshi, Saurabh Kataria, Jesus Villalba +1
Adversarial attacks pose a severe security threat to the state-of-the-art speaker identification systems, thereby making it vital to propose countermeasures against them. Building…
Representation Learning to Classify and Detect Adversarial Attacks against Speaker and Speech Recognition Systems
Jesús Villalba, Sonal Joshi, Piotr Żelasko +1
Adversarial attacks have become a major threat for machine learning applications. There is a growing interest in studying these attacks in the audio domain, e.g, speech and speaker…
Adversarial Attacks and Defenses for Speech Recognition Systems
Piotr Żelasko, Sonal Joshi, Yiwen Shao +4
The ubiquitous presence of machine learning systems in our lives necessitates research into their vulnerabilities and appropriate countermeasures. In particular, we investigate the…
Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems
Sonal Joshi, Jesús Villalba, Piotr Żelasko +2
Adversarial examples to speaker recognition (SR) systems are generated by adding a carefully crafted noise to the speech signal to make the system fail while being imperceptible to…