# Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence

arXiv: https://arxiv.org/abs/2402.14982 · HTML: https://mlsanity.com/paper/2402.14982/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [ADD 2022: the First Audio Deep Synthesis Detection Challenge](https://mlsanity.com/paper/2202.08433/) (2022)
- [ICLabel: An automated electroencephalographic independent component classifier, dataset, and website](https://mlsanity.com/paper/1901.07915/) (2019)
- [Improving Position Encoding of Transformers for Multivariate Time Series Classification](https://mlsanity.com/paper/2305.16642/) (2023)
- [Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization](https://mlsanity.com/paper/2305.01979/) (2023)
- [An Initial Investigation for Detecting Partially Spoofed Audio](https://mlsanity.com/paper/2104.02518/) (2021)
- [Using Human Brain Activity to Guide Machine Learning](https://mlsanity.com/paper/1703.05463/) (2017)

## Abstract (arXiv)

In this paper we study the variations in human brain activity when listening to real and fake audio. Our preliminary results suggest that the representations learned by a state-of-the-art deepfake audio detection algorithm, do not exhibit clear distinct patterns between real and fake audio. In contrast, human brain activity, as measured by EEG, displays distinct patterns when individuals are exposed to fake versus real audio. This preliminary evidence enables future research directions in areas such as deepfake audio detection.