Universal adversarial audio perturbations, found by a penalty-based optimizer, misclassify over 85% of test sounds across several 1D CNN audio classifiers, in both targeted and untargeted settings.
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Speech and speaker recognition systems are employed in a variety of applications, from personal assistants to telephony surveillance and biometric authentication. The wide deployment of these systems has been made possible by the improved accuracy in neural networks. Like other systems based on neural networks, recent research has demonstrated that speech and speaker recognition systems are vulnerable to attacks using manipulated inputs. However, as we demonstrate in this paper, the end-to-end architecture of speech and speaker systems and the nature of their inputs make attacks and defenses against them substantially different than those in the image space. We demonstrate this first by systematizing existing research in this space and providing a taxonomy through which the community can evaluate future work. We then demonstrate experimentally that attacks against these models almost universally fail to transfer. In so doing, we argue that substantial additional work is required to provide adequate mitigations in this space.
fields
cs.LG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Universal Adversarial Audio Perturbations
Universal adversarial audio perturbations, found by a penalty-based optimizer, misclassify over 85% of test sounds across several 1D CNN audio classifiers, in both targeted and untargeted settings.