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- W4315705628 abstract "With the rapid development of deep learning, its vulnerability has gradually emerged in recent years. This work focuses on backdoor attacks on speech recognition systems. We adopt sounds that are ordinary in nature or in our daily life as triggers for natural backdoor attacks. We conduct experiments on two datasets and three models to validate the performance of natural backdoor attacks and explore the effects of poisoning rate, trigger duration and blend ratio on the performance of natural backdoor attacks. Our results show that natural backdoor attacks have a high attack success rate without compromising model performance on benign samples, even with short or low-amplitude triggers. It requires only 5% of poisoned samples to achieve a near 100% attack success rate. In addition, the backdoor will be automatically activated by the corresponding sound in nature, which is not easy to be detected and will bring severer harm." @default.
- W4315705628 created "2023-01-12" @default.
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- W4315705628 date "2023-01-01" @default.
- W4315705628 modified "2023-10-02" @default.
- W4315705628 title "Natural Backdoor Attacks on Speech Recognition Models" @default.
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- W4315705628 doi "https://doi.org/10.1007/978-3-031-20096-0_45" @default.
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