Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence
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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.
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References · 8
- 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 561
- 2021YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyonecited by 278
- 2022ADD 2022: the First Audio Deep Synthesis Detection Challengecited by 96
- 2019ICLabel: An automated electroencephalographic independent component classifier, dataset, and websitecited by 46
- 2023Improving Position Encoding of Transformers for Multivariate Time Series Classificationcited by 45
- 2023Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localizationcited by 43
- 2021An Initial Investigation for Detecting Partially Spoofed Audiocited by 27
- 2017Using Human Brain Activity to Guide Machine Learningcited by 15
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