  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).
