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DETECTION OF SPOOFING ATTACKS ON SPEAKER VERIFICATION SYSTEMS IN TELEPHONE CHANNEL

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Subject of Research. The present paper is devoted to the attacks detection problem  on voice biometric systems (spoofing-attacks) in telephone channel. Nowadays, spoofing detection is under the high interest in the field of voice speaker authentication. The results of the Automatic Speaker Verification Spoofing and Countermeasures Challenge in 2015 and 2017 dedicated to isolated task of spoofing detection confirmed the high perspectives in detection of unknown types of attacks in microphone channel. However, similar task in telephone channel remains extremely relevant, for example, in the banking sector. Method. The aim of the work was to study the applicability of deep learning approach for described problem solution, in particular, convolutional neural networks with the Max-Feature-Map activation function.Main Results.The experiments performed for real telephone attacks showed insufficient efficiency of the systems trained on data with emulated telephone channel. That is why, the database of real spoofing attacks in telephone channel was collected. The best system demonstrated 1.5% equal error rate (EER) on a subset of replay attacks, 1.7% for voice conversion attacks, and 2.8% for attacks with voice synthesis. Experiments show the need to consider different recording conditions, due to the great number of factors that have the influence on the channel. Practical Relevance.The results of the work can be applied in the field of voice biometrics. The presented methods can be used in systems of automatic speaker verification and identification for detection of spoofing attacks on them.

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