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- W4378498725 abstract "Large-scale pretrained models using self-supervised learning have reportedly improved the performance of speech anti-spoofing. However, the attacker side may also make use of such models. Also, since it is very expensive to train such models from scratch, pretrained models on the Internet are often used, but the attacker and defender may possibly use the same pretrained model. This paper investigates whether the improvement in anti-spoofing with pretrained models holds under the condition that the models are available to attackers. As the attacker, we train a model that enhances spoofed utterances so that the speaker embedding extractor based on the pretrained models cannot distinguish between bona fide and spoofed utterances. Experimental results show that the gains the anti-spoofing models obtained by using the pretrained models almost disappear if the attacker also makes use of the pretrained models." @default.
- W4378498725 created "2023-05-27" @default.
- W4378498725 creator A5004043077 @default.
- W4378498725 creator A5026324656 @default.
- W4378498725 date "2023-05-24" @default.
- W4378498725 modified "2023-09-25" @default.
- W4378498725 title "Spoofing Attacker Also Benefits from Self-Supervised Pretrained Model" @default.
- W4378498725 doi "https://doi.org/10.48550/arxiv.2305.15518" @default.
- W4378498725 hasPublicationYear "2023" @default.
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