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Found 6 papers, 1 papers with code
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models
To exploit ASR models in real-world, black-box settings, an adversary can leverage the transferability property, i. e. that an adversarial sample produced for a proxy ASR can also fool a different remote ASR.
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems
We begin by comparing 20 recent attack papers, classifying and measuring their suitability to serve as the basis of new "robust to transcription" but "easy for humans to understand" CAPTCHAs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems
The targeted transferability of adversarial samples enables attackers to exploit black-box models in the real-world.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems
Like other systems based on neural networks, recent research has demonstrated that speech and speaker recognition systems are vulnerable to attacks using manipulated inputs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems
Automatic speech recognition and voice identification systems are being deployed in a wide array of applications, from providing control mechanisms to devices lacking traditional interfaces, to the automatic transcription of conversations and authentication of users.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems
In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems.
