most citedDefense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser

3 citations · 3 across the 3 of their papers we have counts for

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3 papers

eess.AS2022★ 3 cited

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…

eess.AS2022

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…

eess.AS2021

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…