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- W4313447224 abstract "Audio DeepFakes (DF) are artificially generated utterances created using deep learning, with the primary aim of fooling the listeners in a highly convincing manner. Their quality is sufficient to pose a severe threat in terms of security and privacy, including the reliability of news or defamation. Multiple neural network-based methods to detect generated speech have been proposed to prevent the threats. In this work, we cover the topic of adversarial attacks, which decrease the performance of detectors by adding superficial (difficult to spot by a human) changes to input data. Our contribution contains evaluating the robustness of 3 detection architectures against adversarial attacks in two scenarios (white-box and using transferability) and enhancing it later by using adversarial training performed by our novel adaptive training. Moreover, one of the investigated architectures is RawNet3, which, to the best of our knowledge, we adapted for the first time to DeepFake detection." @default.
- W4313447224 created "2023-01-06" @default.
- W4313447224 creator A5032526628 @default.
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- W4313447224 date "2022-12-30" @default.
- W4313447224 modified "2023-10-16" @default.
- W4313447224 title "Defense Against Adversarial Attacks on Audio DeepFake Detection" @default.
- W4313447224 doi "https://doi.org/10.48550/arxiv.2212.14597" @default.
- W4313447224 hasPublicationYear "2022" @default.
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