Creation and Detection of German Voice Deepfakes

TL;DR

This paper examines the ease of creating convincing German voice deepfakes, analyzes current methods, and evaluates detection challenges, highlighting societal risks and potential detection techniques in online teaching contexts.

Contribution

It provides a detailed analysis of voice deepfake creation requirements, technical guidance, and an empirical user study on detection difficulty in German language scenarios.

Findings

Only 37% of participants could distinguish real from fake voices.

Creating convincing voice deepfakes requires minimal training data and effort.

Detection of deepfakes remains challenging, especially in German language contexts.

Abstract

Synthesizing voice with the help of machine learning techniques has made rapid progress over the last years [1] and first high profile fraud cases have been recently reported [2]. Given the current increase in using conferencing tools for online teaching, we question just how easy (i.e. needed data, hardware, skill set) it would be to create a convincing voice fake. We analyse how much training data a participant (e.g. a student) would actually need to fake another participants voice (e.g. a professor). We provide an analysis of the existing state of the art in creating voice deep fakes, as well as offer detailed technical guidance and evidence of just how much effort is needed to copy a voice. A user study with more than 100 participants shows how difficult it is to identify real and fake voice (on avg. only 37 percent can distinguish between real and fake voice of a professor). With a focus on German language and an online teaching environment we discuss the societal implications as well as demonstrate how to use machine learning techniques to possibly detect such fakes.