Adversarial Attacks on GMM i-vector based Speaker Verification Systems
This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based speaker verification systems to adversarial attacks, and the transferability of adversarial samples crafted from GMM i-vector based systems to x-vector based systems. In detail, we formulate the GMM i-vector system as a scoring function of enrollment and testing utterance pairs. Then we leverage the fast gradient sign method (FGSM) to optimize testing utterances for adversarial samples generation. These adversarial samples are used to attack both GMM i-vector and x-vector systems. We measure the system vulnerability by the degradation of equal error rate and false acceptance rate. Experiment results show that GMM i-vector systems are seriously vulnerable to adversarial attacks, and the crafted adversarial samples prove to be transferable and pose threats to neuralnetwork speaker embedding based systems (e.g. x-vector systems).
Code (2)
Tasks
Speaker VerificationSimilar Papers 제목 키워드 기반
On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems
Speaker verification systems have been widely used in smart phones and Internet of things devices to identify legitimate users. In recent work, it has been shown that adversarial attacks, such as FAKEBOB, can work effect…
Speaker VerificationDictionary Attacks on Speaker Verification
In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that…
Speaker VerificationVoice CloningFooling End-to-end Speaker Verification by Adversarial Examples
Automatic speaker verification systems are increasingly used as the primary means to authenticate costumers. Recently, it has been proposed to train speaker verification systems using end-to-end deep neural models. In th…
Speaker VerificationMalacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model
We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automatic speaker verification (ASV) systems. U…
Adversarial AttackSpeaker VerificationAdversarial Attacks on Spoofing Countermeasures of automatic speaker verification
High-performance spoofing countermeasure systems for automatic speaker verification (ASV) have been proposed in the ASVspoof 2019 challenge. However, the robustness of such systems under adversarial attacks has not been …
Speaker Verification