Search Results for author:
Found 9 papers, 5 papers with code
A Proactive and Dual Prevention Mechanism against Illegal Song Covers empowered by Singing Voice Conversion
To improve the imperceptibility of perturbations, we refine a psychoacoustic model-based loss with the backing track as an additional masker, a unique accompanying element for singing voices compared to ordinary speech voices.
SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems
Our attack is versatile and can work in both white-box and black-box scenarios.
QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systems
Current adversarial attacks against speaker recognition systems (SRSs) require either white-box access or heavy black-box queries to the target SRS, thus still falling behind practical attacks against proprietary commercial APIs and voice-controlled devices.
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition
According to the characteristic of SRSs, we present 22 diverse transformations and thoroughly evaluate them using 7 recent promising adversarial attacks (4 white-box and 3 black-box) on speaker recognition.
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems
Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against adversarial attacks, raising significant security concerns in deploying SRSs.
SEC4SR: A Security Analysis Platform for Speaker Recognition
To bridge this gap, we present SEC4SR, the first platform enabling researchers to systematically and comprehensively evaluate adversarial attacks and defenses in SR. SEC4SR incorporates 4 white-box and 2 black-box attacks, 24 defenses including our novel feature-level transformations.
Attack as Defense: Characterizing Adversarial Examples using Robustness
Though various defense mechanisms have been proposed to improve robustness of deep learning software, many of them are ineffective against adaptive attacks.
BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks
Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications.
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
In this paper, we conduct the first comprehensive and systematic study of the adversarial attacks on SR systems (SRSs) to understand their security weakness in the practical blackbox setting.
