Pindrop Deepfake detection holds promises With a Shannon frequency of only 4 kHz (half of the sampling rate), a good portion of the relevant information narrowband audio is missing, making the discriminative ability of the standard models much more reduced.

While the task of detecting deepfake audio on wideband audio is still hard, particularly when it comes to newer and more sophisticated attacks, the problem is significantly more challenging for narrowband call center audio signals. Wavenet of DeepMind, Tacatron of Google, or FastSpeech of Microsoft).Īdditionally, the detection of wideband deepfake audio has been the focus of the research community where academic and industrial competitions such as the ASVspoof were conducted since 2015, and where Pindrop technologies always showcase high performance and good generalization ability. Wideband audio signals not only enable high/studio-quality sound, but also inherently allow to carry audio artifacts in the high frequencies that are very relevant to detect a deepfake by a software such as Pindrop’s deep learning based deepfake detector, or even sometimes by the human ear, particularly when it comes to fricatives such as /s/, /f/, or /v/ that are typically not well produced by speech synthesis systems (e.g. In Anthony Bourdain’s video, the audio is categorized as wideband where the sampling rate is higher than 16 kHz.

Call center deepfake attacks are most challenging This is more difficult using text-to-speech techniques. At the time of writing this blog, only little details about the $35 Million bank heist are public knowledge, but Pindrop suspects that voice conversion was used in this attack because a live call and natural conversation took place between the attacker and the victim’s bank manager. It is however used in some social media applications to convert someone’s voice to a celebrity voice. This technique is less known to the public because of the limited real-word applications. attacker’s voice) into a target speaker’s voice (e.g. Voice conversion consists of converting a source speaker’s voice (e.g. Siri, Amazon Echo, Google Home), and more recently to help individuals who lost their voices.

Speech synthesis typically aims to convert a text to speech and is well deployed in real world applications like smart devices and voice assistants (e.g. Voice conversion as a fraud toolĭeepfake Audio includes two types of speech processing techniques: speech synthesis and voice conversion.

This time, it was a deepfake audio – contrary to Anthony Bourdain’s deepfake, this attack happened over the phone channel. Another synthetic fraudster attack was in the news recently.

Voice deepfake app