The proliferation of audio deepfakes poses significant risks to modern society, ranging from misinformation to secu- rity threats. As deepfake technology continues to advance, the need for robust detection mech- anisms becomes critical. This thesis introduces a novel approach for detecting audio deepfakes utilizing implicit neural representations. By leveraging these representations, we aim to capture and analyze the unique patterns inherent in audio, particularly focusing on the segments of si- lence. The performances of the model are close to the state-of-the-art, proving the effectiveness of this methodology. To assess the ability of humans and algorithms to detect audio deepfakes, perceptive tests in a controlled environment were performed, and compared against a baseline model. The results show that human testers perform poorly, in comparison to the baseline model. These insights might be helpful for future research.
The proliferation of audio deepfakes poses significant risks to modern society, ranging from misinformation to secu- rity threats. As deepfake technology continues to advance, the need for robust detection mech- anisms becomes critical. This thesis introduces a novel approach for detecting audio deepfakes utilizing implicit neural representations. By leveraging these representations, we aim to capture and analyze the unique patterns inherent in audio, particularly focusing on the segments of si- lence. The performances of the model are close to the state-of-the-art, proving the effectiveness of this methodology. To assess the ability of humans and algorithms to detect audio deepfakes, perceptive tests in a controlled environment were performed, and compared against a baseline model. The results show that human testers perform poorly, in comparison to the baseline model. These insights might be helpful for future research.
Audio Deepfake Detection based on Implicit Neural Representations
SIMONATO, NICCOLÒ
2023/2024
Abstract
The proliferation of audio deepfakes poses significant risks to modern society, ranging from misinformation to secu- rity threats. As deepfake technology continues to advance, the need for robust detection mech- anisms becomes critical. This thesis introduces a novel approach for detecting audio deepfakes utilizing implicit neural representations. By leveraging these representations, we aim to capture and analyze the unique patterns inherent in audio, particularly focusing on the segments of si- lence. The performances of the model are close to the state-of-the-art, proving the effectiveness of this methodology. To assess the ability of humans and algorithms to detect audio deepfakes, perceptive tests in a controlled environment were performed, and compared against a baseline model. The results show that human testers perform poorly, in comparison to the baseline model. These insights might be helpful for future research.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/80281