Detecting Deepfake Voice Using Explainable Deep Learning Techniques

Fake media, generated by methods such as deepfakes, have become indistinguishable from real media, but their detection has not improved at the same pace. Furthermore, the absence of interpretability on deepfake detection models makes their reliability questionable. In this paper, we present a human...

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Bibliographic Details
Published inApplied sciences Vol. 12; no. 8; p. 3926
Main Authors Lim, Suk-Young, Chae, Dong-Kyu, Lee, Sang-Chul
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2022
Subjects
Artificial intelligence
Classification
Datasets
Deception
Deep learning
deepfake detection
explainable artificial intelligence (XAI)
human-centered artificial intelligence
Image classification
Noise
Speech
Voice recognition
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Summary:Fake media, generated by methods such as deepfakes, have become indistinguishable from real media, but their detection has not improved at the same pace. Furthermore, the absence of interpretability on deepfake detection models makes their reliability questionable. In this paper, we present a human perception level of interpretability for deepfake audio detection. Based on their characteristics, we implement several explainable artificial intelligence (XAI) methods used for image classification on an audio-related task. In addition, by examining the human cognitive process of XAI on image classification, we suggest the use of a corresponding data format for providing interpretability. Using this novel concept, a fresh interpretation using attribution scores can be provided.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12083926