High-Performance Fake Voice Detection on Automatic Speaker Verification Systems for the Prevention of Cyber Fraud with Convolutional Neural Networks
Ricardo Buettner, Jan Gross, Philipp Roessler, Julia Winter, Daniel Sauter, Hermann Baumgartl, Patrick Ulrich · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2022
This study proposes a highly effective data analytics approach to prevent cyber fraud on automatic speaker verification systems by classifying histograms of genuine and spoofed voice recordings. Our deep learning-based lightweight architecture advances the application of fake voice detection on embedded systems. It sets a new benchmark with a balanced accuracy of 95.64% and an equal error rate of 4.43%, contributing to adopting artificial intelligence technologies in organizational systems and technologies. As fake voice-related fraud causes monetary damage and serious privacy concerns for various applications, our approach improves the security of such services, being of high practical relevance. Furthermore, the post-hoc analysis of our results reveals that our model confirms image texture analysis-related findings of prior studies and discovers further voice signal features (i.e., textural and contextual) that can advance future work in this field.