SiFDetectCracker: An Adversarial Attack Against Fake Voice Detection Based on Speaker-Irrelative Features

Abstract

Voice is a vital medium for transmitting information. The advancement of speech synthesis technology has resulted in high-quality synthesized voices indistinguishable from human ears. These fake voices have been widely used in natural Deepfake production and other malicious activities, raising serious concerns regarding security and privacy. To deal with this situation, there have been many studies working on detecting fake voices and reporting excellent performance. However, is the story really over? In this paper, we propose SiFDetectCracker, a black-box adversarial attack framework based on Speaker-Irrelative Features (SiFs) against fake voice detection. We select background noise and mute parts before and after the speaker's voice as the primary attack features. By modifying these features in synthesized speech, the fake speech detector will make a misjudgment. Experiments show that SiFDetectCracker achieved a success rate of more than 80% in bypassing existing state-of-the-art fake voice detection systems. We also conducted several experiments to evaluate our attack approach's transferability and activation factor.