LOGICAL ACCESS ATTACKS DETECTION THROUGH AUDIO FINGERPRINTING IN AUTOMATIC SPEAKER VERIFICATION

Automatic Speaker Verification (ASV) is being implemented in many applications, where maximum security and robustness against attacks must be guaranteed. One of the most challenging attacks that an ASV system can face is the so called "logical access attack", in which the attacker has the possibility to directly inject a compromised audio sample into the system. The development of countermeasures for this kind of attack has received little attention to date. When the injected audio is identical to a sample previously seen by the system, current audio fingerprinting techniques can detect most of these attacks. However, we show that, with trivial modifications that do not require any special signal processing knowledge, the audio can bypass these countermeasures while keeping the ability of the ASV system to authenticate the user. To address this issue, we propose an alternative method and validate it against a variety of audio perturbations. It generalizes previous fingerprinting techniques and acquire robustness against changes of tempo.