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- W3193438067 abstract "This paper proposes a new defense approach for counteracting state-of-the-art white and black-box adversarial attack algorithms. Our approach fits into the implicit reactive defense algorithm category since it does not directly manipulate the potentially malicious input signals. Instead, it reconstructs a similar signal with a synthesized spectrogram using a cyclic generative adversarial network. This cyclic framework helps to yield a stable generative model. Finally, we feed the reconstructed signal into the speech-to-text model for transcription. The conducted experiments on targeted and non-targeted adversarial attacks developed for attacking DeepSpeech, Kaldi, and Lingvo models demonstrate the proposed defense's effectiveness in adverse scenarios." @default.
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- W3193438067 date "2021-01-01" @default.
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- W3193438067 title "Cyclic Defense GAN Against Speech Adversarial Attacks" @default.
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- W3193438067 doi "https://doi.org/10.1109/lsp.2021.3106239" @default.
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